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Bud rot (BR), caused by Phytophthora palmivora, limits growth and development of oil palms in the American continent, particularly in Colombia. Due to the absence of systematic epidemiological analyses and determination of risk factors associated with BR, this study aimed to employ deterministic mathematical models and frequentist and Bayesian statistical methods to quantify the genetic response and edaphoclimatic variables as risk indicators of BR. From 2011 to 2014, the severity of BR in oil palm crops was recorded monthly in two locations: Tumaco (hot spot) and Villavicencio (cold spot), determining the edaphoclimatic variables at each site. Using the area under the disease progress stairs curve, temporal models were applied to determine the rate of disease development (R0) to quantify risk at locality and genotype levels. Subsequently, the observed intensity and severity levels of BR were adjusted to models such as survival curves, Cox proportional hazard risk and transition probabilities, or Markov states, with the aim of quantifying and characterizing risk factors associated with genotype and edaphoclimatic variables at each location. The risk in Tumaco was three times higher than in Villavicencio, and the R0 values were differential by genotype, with Tenera more susceptible than Hybrids. Moreover, the risk of BR increased when periods of 2–3 months occurred with successive instances of precipitation and relative humidity greater than 150 mm/month and 90%, respectively, and when manganese and zinc levels were below optimal. This approach allowed us to characterize epidemiological factors that cause plant diseases, allowing quantification of the risk of BR. [ABSTRACT FROM AUTHOR]

We consider inference about the parameter that determines the distribution of the data. In frequentist inference a very important and useful idea is that data reduction to a sufficient statistic does not lose any information about this parameter. We recall two justifications for this idea in frequentist inference. We then examine the extent to which these justifications carry over to conditional frequentist inference inference, which consists of carrying out frequentist inference conditional on an ancillary statistic. This examination shows that, in the context of conditional frequentist inference, first reducing data to a sufficient statistic is not always justified, so we should first condition on an ancillary statistic. Finally, we describe two types of practically important statistical models that illustrate this finding. [ABSTRACT FROM AUTHOR]

In management research, fixed alpha levels in statistical testing are ubiquitous. However, in highly powered studies, they can lead to Lindley's paradox, a situation where the null hypothesis is rejected despite evidence in the test actually supporting it. We propose a sample-size-dependent alpha level that combines the benefits of both frequentist and Bayesian statistics, enabling strict hypothesis testing with known error rates while also quantifying the evidence for a hypothesis. We offer actionable guidelines of how to implement the sample-size-dependent alpha in practice and provide an R-package and web app to implement our method for regression models. By using this approach, researchers can avoid mindless defaults and instead justify alpha as a function of sample size, thus improving the reliability of statistical analysis in management research. [ABSTRACT FROM AUTHOR]

This article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self‐sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with each other and with those from standard machine learning using satellite data. [ABSTRACT FROM AUTHOR]

Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p‐value. Null hypothesis Bayesian testing and its so‐called Bayes factor are now becoming increasingly popular. Although the Bayes factor is often introduced as being the Bayesian counterpart to the p‐value, its computation, use, and interpretation are quite distinct from the p‐value. There is now evidence confirming that the application of the Bayes factor in applied research is ill‐devised. To improve the current status quo, we have created a Shiny/R app called the Bayes factor, which offers a dynamic tutorial for learning all the basics about the Bayes factor. In this paper, we explain how the app works and we offer suggestions on how to use it in class or self‐study settings. The app is freely available at https://statsedge.org/shiny/LearnBF/. [ABSTRACT FROM AUTHOR]

Background: Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis. Methods: We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown. Results: Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamohippocampal network entails a larger share of D1--as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs. Conclusion: Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies. [ABSTRACT FROM AUTHOR]

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work. [ABSTRACT FROM AUTHOR]

Ischemic preconditioning (IPC), which involves episodes of blood flow restriction followed by reperfusion, has uncertain effects on athletes. Additionally, employing sports-specific tests that are highly familiar to athletes can enhance methodological rigor in determining IPC's effects on taekwondo performance. This study aimed to investigate IPC's influence on taekwondo athletes' performance through two studies. To induce occlusion in both studies, the cuff was inflated to an individualized occlusion pressure established for each athlete's lower limb, with four cycles of occlusion lasting five minutes each, alternated with five-minute reperfusion intervals. Both traditional frequentist statistics and Bayesian analysis were employed. In the first study, eleven high-level athletes were subjected to either IPC or a placebo (SHAM) procedure on both legs, followed by performing countermovement jumps (CMJs) and a specific taekwondo endurance test. However, no significant differences were observed in taekwondo endurance performance or CMJ between the IPC and SHAM conditions. The second study involved fourteen elite athletes who underwent the same IPC or SHAM conditions, performing CMJ and three bouts of the Multiple Frequency Speed of Kick test (FSKTmult) in three blocks, each separated by approximately thirty minutes. Again, the results indicated no significant differences in FSKTmult measures or CMJ performance between the two conditions. In conclusion, IPC did not significantly affect neuromuscular (in both studies), endurance (in the first study), or anaerobic (in the second study) performance in these taekwondo athletes. [ABSTRACT FROM AUTHOR]

Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal, 28, 1–14, (2021a, 2021b) proposed a variant of the Wald test that uses Markov chain Monte Carlo machinery to generate a chi-square test statistic for frequentist inference. Because the test's composition does not rely on analytic expressions for sampling variation and covariation, it potentially provides a way to get honest significance tests in cases where the likelihood-based test statistic's assumptions break down (e.g., in small samples). The goal of this study is to use simulation to compare the new MCM Wald test to its maximum likelihood counterparts, with respect to both their type I error rate and power. Our simulation examined the test statistics across different levels of sample size, effect size, and degrees of freedom (test complexity). An additional goal was to assess the robustness of the MCMC Wald test with nonnormal data. The simulation results uniformly demonstrated that the MCMC Wald test was superior to the maximum likelihood test statistic, especially with small samples (e.g., sample sizes less than 150) and complex models (e.g., models with five or more predictors). This conclusion held for nonnormal data as well. Lastly, we provide a brief application to a real data example. [ABSTRACT FROM AUTHOR]

Previous works have suggested a high prevalence of painkiller intake (PI) among sport students but also improved health literacy (HL) for sports-active students than for sports-inactive students. Since health-related content also forms part of the sport science curriculum, the study focuses on these seemingly paradoxical results. Music students who are also physically active through their instrumental practice, who act in an area with increased PI and who have no health-related teaching content in their curriculum composed the comparison group. Therefore, this study investigated the prevalence of PI and HL in cohorts of sport (n = 222; 54.5% female) and music students (n = 89; 67.4% female) using a cross-sectional online survey in Lower Saxony, Germany. The hypothesis tests were validated by calculating frequentist and Bayesian statistics. The results show that 50.9% of sport and 28.1% of music students exhibit PI concerning their study programs, often for prophylactical purposes and in the presence of low HL levels. The weak negative correlation between PI and HL was not statistically confirmed and requires further research with improved test power. Regarding the possible health consequences of an inconsiderate PI, target group-specific prevention is indicated to increase general health awareness and HL. [ABSTRACT FROM AUTHOR]

Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the power spectrum or correlation function(s). Sample variance is particularly acute at large scales, which is also where analytic techniques can be highly reliable. This provides an opportunity to combine analytic and numerical techniques in a principled way to improve the dynamic range and reliability of predictions for clustering statistics. In this paper we extend the technique of Zel'dovich control variates, previously demonstrated for 2-point functions in real space, to reduce the sample variance in measurements of 2-point statistics of biased tracers in redshift space. We demonstrate that with this technique, we can reduce the sample variance of these statistics down to their shot-noise limit out to k ∼ 0.2 h Mpc-1. This allows a better matching with perturbative models and improved predictions for the clustering of e.g. quasars, galaxies and neutral Hydrogen measured in spectroscopic redshift surveys at very modest computational expense. We discuss the implementation of ZCV, give some examples and provide forecasts for the efficacy of the method under various conditions.

An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior–like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials. [ABSTRACT FROM AUTHOR]

Background: Human brucellosis is a neglected, re-emerging, and endemic zoonosis in many countries. The debilitating and disabling potential of the disease is a warning about its morbidity, generating socioeconomic impact. This review aims to update the current evidence on the efficacy and safety of therapeutic options for human brucellosis using the network meta-analysis (NMA). Methodology: A systematic search was conducted in four different databases by independent reviewers to assess overall therapy failure, adverse events, and time to defervescence associated with different therapies. Randomized clinical trials (RCTs) evaluating any therapeutic drug intervention were selected, excluding non-original studies or studies related to localized forms of the disease or with less than 10 participants. Data were analyzed by frequentist statistics through NMA by random effects model. The risk of bias and certainty of evidence was assessed, this review was registered at PROSPERO. Results: Thirty-one (31) RCTs involving 4167 patients were included. Three networks of evidence were identified to evaluate the outcomes of interest. Triple therapy with doxycycline + streptomycin + hydroxychloroquine for 42 days (RR: 0.08; CI 95% 0.01–0.76) had a lower failure risk than the doxycycline + streptomycin regimen. Doxycycline + rifampicin had a higher risk of failure than doxycycline + streptomycin (RR: 1.96; CI 95% 1.27–3.01). No significant difference was observed between the regimens when analyzing the incidence of adverse events and time to defervescence. In general, most studies had a high risk of bias, and the results had a very low certainty of evidence. Conclusions: This review confirmed the superiority of drugs already indicated for treating human brucellosis, such as the combination of doxycycline and aminoglycosides. The association of hydroxychloroquine to the dual regimen was identified as a potential strategy to prevent overall therapy failure, which is subject to confirmation in future studies. Author summary: Human brucellosis it is a disease with transmission associated with the handling of animals and ingestion of contaminated products. The disease usually presents with a subacute to chronic febrile condition characterized by nonspecific symptoms such weight loss, and sudoresis. Therapeutic regimens combining two or more drugs have been shown to be superior to monotherapy. The debilitating and disabling potential of the disease is a warning about its morbidity, whose symptoms can persist for weeks or months, generating socioeconomic impact. So far, the accumulated evidence is based on clinical trials that compared the interventions of interest against different comparators. The authors this reviewed all published studies and performed an analysis was capable of comparing the effect of therapeutic regimens to identify the best combinations to treat human brucellosis. The combination of three medicines (doxycycline + streptomycin + hydroxychloroquine) showed the best results compared to the option previously identified as more effective (doxycycline + streptomycin), so this research points to new perspectives for the treatment of brucellosis. This result point that more studies dedicated to studying this combination must be developed to confirm this trend and enable the adoption of new practices in the treatment of this disease. [ABSTRACT FROM AUTHOR]

Background: p value is the most common statistic reported in scientific research articles. Choosing the conventional threshold of 0.05 commonly used for the p value in research articles, is unfounded. Many researchers have tried to provide a reasonable threshold for the p value; some proposed a lower threshold, eg, 0.005. However, none of the proposals has gained universal acceptance. Using the analogy between the diagnostic tests with continuous results and statistical inference tests of hypothesis, I wish to present a method to calculate the most appropriate p value significance threshold using the receiver operating characteristic curve (ROC) analysis. Results: As with diagnostic tests where the most appropriate cut-off values are different depending on the situation, there is no unique cut-off for the p significance threshold. Unlike the previous proposals, which mostly suggest lowering the threshold to a fixed value (eg, from 0.05 to 0.005), the most appropriate p significance threshold proposed here, in most instances, is much less than the conventional cut-off of 0.05 and varies from study to study and from statistical test to test, even within a single study. The proposed method provides the minimum weighted sum of type I and type II errors. Conclusions: Given the perplexity involved in using the frequentist statistics in a correct way (dealing with different p significance thresholds, even in a single study), it seems that the p value is no longer a proper statistic to be used in our research; it should be replaced by alternative methods, eg, Bayesian methods. [ABSTRACT FROM AUTHOR]

Objective: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. Method: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre–post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ‐30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. Results: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. Conclusion: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area. [ABSTRACT FROM AUTHOR]

The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and inference. It is argued that this contributes to the untrustworthiness problem in several different ways, including [a] statistical misspecification, [b] unwarranted evidential interpretations of frequentist inference results, and [c] questionable modeling strategies that rely on curve-fitting. What is more, the alternative proposals to replace or modify frequentist testing, including [i] replacing p-values with observed confidence intervals and effects sizes, and [ii] redefining statistical significance, will not address the untrustworthiness of evidence problem since they are equally vulnerable to [a]–[c]. The paper calls for distinguishing between unduly data-dependant 'statistical results', such as a point estimate, a p-value, and accept/reject H 0 , from 'evidence for or against inferential claims'. The post-data severity (SEV) evaluation of the accept/reject H 0 results, converts them into evidence for or against germane inferential claims. These claims can be used to address/elucidate several foundational issues, including (i) statistical vs. substantive significance, (ii) the large n problem, and (iii) the replicability of evidence. Also, the SEV perspective sheds light on the impertinence of the proposed alternatives [i]–[iii], and oppugns [iii] the alleged arbitrariness of framing H 0 and H 1 which is often exploited to undermine the credibility of frequentist testing. [ABSTRACT FROM AUTHOR]

The Social Readjustment Rating Scale, originally devised in 1967 by Holmes and Rahe, measures the impact of life events stress. At the time, the SRRS advanced its field of research by standardising the impact of stress with a set of independently derived weights called 'life change units' (LCUs) for 43 life events found to predict illness onset. The scale has been criticised for being outdated, e.g. "Mortgage over $10,000" and biased, e.g. "Wife begin or stop work". The aim of this cross-sectional survey study is to update and improve the SRRS whilst allowing backwards compatibility. We successfully updated the SRRS norms/LCUs using the ratings of 540 predominantly UK adults aged 18 to 84. Moreover, we also updated wording of 12 SRRS items and evaluated the impact of demographics, personal experience and loneliness. Using non-parametric frequentist and Bayesian statistics we found that the updated weights were higher but broadly consistent with those of the original study. Furthermore, changes to item wording did not affect raters' evaluations relative to the original thereby ensuring cross-comparability with the original SRRS. The raters were not unduly influenced by their personal experiences of events nor loneliness. The target sample was UK rather than US-based and was proportionately representative regarding age, sex and ethnicity. Moreover, the age range was broader than the original SRRS. In addition, we modernised item wording, added one optional extra item to the end of the scale to evaluate the readjustment to living alone and identified 3 potential new items proposed by raters. Backwards-compatibility is maintained. [ABSTRACT FROM AUTHOR]

We define confidence to be epistemic if it applies to an observed confidence interval. Epistemic confidence is unavailable—or even denied—in orthodox frequentist inference, as the confidence level is understood to apply to the procedure. Yet there are obvious practical and psychological needs to think about the uncertainty in the observed interval. We extend the Dutch Book argument used in the classical Bayesian justification of subjective probability to a stronger market‐based version, which prevents external agents from exploiting unused information in any relevant subset. We previously showed that confidence is an extended likelihood, and the likelihood principle states that the likelihood contains all the information in the data, hence leaving no relevant subset. Intuitively, this implies that confidence associated with the full likelihood is protected from the Dutch Book, and hence is epistemic. Our goal is to validate this intuitive notion through theoretical backing and practical illustrations. [ABSTRACT FROM AUTHOR]

Purpose: To define and compare the effectiveness of external dacryocystorhinostomy (EX-DCR), endonasal (EN-DCR), and transcanalicular laser-assisted (TL-DCR) with or without silicone stent (S) intubation. Methods: Studies were retrieved from PubMed, Scopus, and WoS. Frequentist and Bayesian network meta-analyses were performed and pooled estimations were expressed as risk ratio (RR). We included all original investigations of prospective, randomized controlled trials comparing success rate for any two of the following six surgical procedures: standard EX-DCR with or without S, cold EN-DCR with or without S, and TL-DCR with or without S. The primary outcome measure was the objective success rate. Results: Thirty-two studies with 3277 cases were included in the final quantitative analysis. TL-DCR with S was inferior to EN-DCR with S (RR: 1.20; 95% CI: 1.05–1.37), EX-DCR with S (RR: 1.17 95% CI: 1.05–1.29), EN-DCR without S (RR: 1.18; 95% CI: 1.03–1.35), and EX-DCR without S (RR: 1.15; 95%CI: 1.05–1.26) in frequentist statistics. No other statistical difference was found between other surgeries. When we excluded studies with additional interventions (nasal and revision surgery) for sensitivity analysis, 23 studies with 2468 cases were included to analysis. The success rates of TL-DCR with S and EN-DCR without S became similar (RR: 1.14 95% CI: 0.99–1.30) but there was no change in other outcomes. Similar results were found in Bayesian statistics. The surface under the cumulative ranking curve was higher for EN-DCR with S (0.75), whereas it was higher for EX-DCR with S (0.56) after sensitivity analysis. Conclusion: Between endoscopic and external and transcanalicular laser without S procedures, there is no statistical difference. The rank probability showed that EN-DCR with S was a more appropriate surgical option when patients with nasal disease were included, whereas EX-DCR with S was a more appropriate surgical option when patients with nasal disease were excluded from the analysis. [ABSTRACT FROM AUTHOR]

When faced with a binary or count outcome, informative hypotheses can be tested in the generalized linear model using the distance statistic as well as modified versions of the Wald, the Score and the likelihood-ratio test (LRT). In contrast to classical null hypothesis testing, informative hypotheses allow to directly examine the direction or the order of the regression coefficients. Since knowledge about the practical performance of informative test statistics is missing in the theoretically oriented literature, we aim at closing this gap using simulation studies in the context of logistic and Poisson regression. We examine the effect of the number of constraints as well as the sample size on type I error rates when the hypothesis of interest can be expressed as a linear function of the regression parameters. The LRT shows the best performance in general, followed by the Score test. Furthermore, both the sample size and especially the number of constraints impact the type I error rates considerably more in logistic compared to Poisson regression. We provide an empirical data example together with R code that can be easily adapted by applied researchers. Moreover, we discuss informative hypothesis testing about effects of interest, which are a non-linear function of the regression parameters. We demonstrate this by means of a second empirical data example. [ABSTRACT FROM AUTHOR]

Rationale: According to theories of embodied cognition, facial mimicry — the spontaneous, low-intensity imitation of a perceived emotional facial expression — is first an automatic motor response, whose accompanying proprioceptive feedback contributes to emotion recognition. Alternative theoretical accounts, however, view facial mimicry as an emotional response to a rewarding stimulus, and/or an affiliative signal, and thus reject the view of an automatic motor copy. Objectives: To contribute to this debate and further investigate the neural basis of facial mimicry, as well as its relation to reward processing, we measured facial reactions to dynamic happy and angry faces after pharmacologically manipulating the opioid and dopamine systems — respectively, thought to subserve 'liking' and 'wanting' of rewards. Methods: In a placebo-controlled, double-blind experiment, 130 volunteers received in a between-subjects design 50 mg of the opioidergic antagonist naltrexone, 400 mg of the dopaminergic antagonist amisulpride, or placebo. Results: Clear occurrence of facial mimicry, measured 4 h after drug intake with electromyography (EMG) of the zygomaticus major and corrugator supercilii muscles, was found. However, facial mimicry was not affected by either compound, as shown with both frequentist statistics, and a Bayesian asymptotic regression model. Conclusions: This null finding does not support the hypothesis that facial mimicry (of happiness) reflects an emotional response to a rewarding stimulus, leaving open the possibility of facial mimicry being an automatic motor copy. The results are relevant to the discussion about the psychological nature and the neural basis of facial mimicry, although they should be considered preliminary, given the challenges of interpreting null findings when targeting a novel effect of unknown size. [ABSTRACT FROM AUTHOR]

When we study any queuing system, the performance measures reflect different features of the system. In the classical M/M/1 queuing system, traffic intensity is perhaps the most important performance measure. We propose a fresh and simple estimator for the same and show that it has nice properties. Our approach is frequentist. This approach has the dual advantage of practical usability and familiarity. Our proposed estimator is attractive as it possesses desirable properties. We have shown how our estimator lends itself to testing of hypothesis. Confidence intervals are constructed. Sample size determination is also discussed. A comparison with a few similar estimators is also performed. [ABSTRACT FROM AUTHOR]

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning "dashboard" system (https://sitkalandslide.org/ , last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers. [ABSTRACT FROM AUTHOR]

In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam's razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two methodologies by using the Bayes factor as a test statistic in the frequentist analysis. We show that the standard frequentist maximum likelihood-ratio test statistic corresponds to the Bayes factor with a non-informative Jeffrey's prior. We also show that mixed priors increase the statistical power in frequentist analyses over the maximum likelihood test statistic. We develop an analytic formalism that does not require expensive simulations and generalize Wilks' theorem beyond its usual regime of validity. In specific limits, the formalism reproduces existing expressions, such as the p-value of linear models and periodograms. We apply the formalism to an example of exoplanet transits, where multiplicity can be more than 107. We show that our analytic expressions reproduce the p-values derived from numerical simulations. We offer an interpretation of our formalism based on the statistical mechanics. We introduce the counting of states in a continuous parameter space using the uncertainty volume as the quantum of the state. We show that both the p-value and Bayes factor can be expressed as an energy versus entropy competition.

Existing small area estimation procedures for count data have important limitations. For instance, an M-quantile-based method is known to be less efficient than model-based procedures if the assumptions of the model hold. Also, frequentist inference procedures for Poisson generalized linear mixed models can be computationally intensive or require approximations. Furthermore, area-level models are incapable of incorporating unit-level covariates. We overcome these limitations by developing a small area estimation procedure for a unit-level gamma-Poisson model. The conjugate form of the model permits computationally simple estimation and prediction procedures. We obtain a closed-form expression for the empirical best predictor of the mean as well as a closed-form mean square error estimator. We validate the procedure through simulations. We illustrate the proposed method using a subset of data from the Iowa Seat-Belt Use survey. [ABSTRACT FROM AUTHOR]

In dominated parametric statistical models, confidence sequences provide conservatively valid frequentist inference directly from a likelihood ratio. They ensure a specific mode of replicability when inference is performed on accumulating data: inferential conclusions that are compatible with a guaranteed probability when the sample is enlarged, in the form of overlapping confidence regions. Here we consider both Robbins' mixture confidence sequences and running maximum likelihood confidence sequences recently considered by Wasserman, Ramdas, and Balakrishnan. We compare through simulation the replicability properties of the two kinds of confidence sequences, evaluating, along a prospected enlargement of the sample, the frequency of incompatible estimation intervals and the frequency of failure of simultaneous coverage of the true parameter value. Moreover, we propose a shortcut to extend the application of mixture confidence sequences to pseudo‐likelihoods, in particular to composite likelihood. The main assumption required is that normal asymptotic theory offers a good approximation to the density of the maximizer of the pseudo‐likelihood. When inference is about a scalar parameter of interest, the computation of the proposed sequence of confidence intervals is straightforward. The method is illustrated by an example with replicability properties evaluated through simulation. [ABSTRACT FROM AUTHOR]

In scientific problems, an appropriate statistical model often involves a large number of canonical parameters. Often times, the quantities of scientific interest are real-valued functions of these canonical parameters. Statistical inference for a specified function of the canonical parameters can be carried out via the Bayesian approach by simply using the posterior distribution of the specified function of the parameter of interest. Frequentist inference is usually based on the profile likelihood for the parameter of interest. When the likelihood function is analytical, computing the profile likelihood is simply a constrained optimization problem with many numerical algorithms available. However, for hierarchical models, computing the likelihood function and hence the profile likelihood function is difficult because of the high-dimensional integration involved. We describe a simple computational method to compute profile likelihood for any specified function of the parameters of a general hierarchical model using data doubling. We provide a mathematical proof for the validity of the method under regularity conditions that assure that the distribution of the maximum likelihood estimator of the canonical parameters is non-singular, multivariate, and Gaussian. [ABSTRACT FROM AUTHOR]

Iliac wing fractures due to lap belt loading have been identified in laboratory tests for almost 50 years and an analysis of recent data suggests these injuries are also occurring in the field. With the introduction of highly autonomous vehicles on the horizon, vehicle manufacturers are exploring open cabin concepts that permit reclined postures and separation of the occupant from the knee bolster and instrument panel. This will result in greater reliance on the lap belt and lap belt/pelvis loading to restrain occupants. No injury criteria exist for iliac wing fractures resulting from lap belt loading like that seen in frontal crash conditions. This study tested the tolerance of isolated iliac wings in a controlled lap belt-like loading environment while incorporating the effect of loading angle after analyzing lap belt loading experiments from a previous study. Twenty-two iliac wings were tested; nineteen of them sustained fracture (exact), but the loading input was insufficient to cause fracture in the other three (right censored). The fracture tolerance of the tested specimens ranged widely (1463–8895 N) and averaged 4091 N (SD 2381 N). Injury risk functions were created by fitting Weibull survival models to data that integrated censored and exact failure observations. [ABSTRACT FROM AUTHOR]

Bayesian statistics is a framework for combining new data with existing forms of information to yield more precise inferences than are possible using the data alone. Its greatest practical advantages are the flexibility it offers in incorporating prior information and beliefs, modeling heterogeneity, modeling latent constructs, and combining multiple data sources. There are two goals of this paper: to introduce accounting researchers to Bayesian inference and distinguish it from classical frequentist inference and to showcase when Bayesian modeling can improve inferences in many applications that are of interest to accounting researchers. Data Availability: Data are available from the public sources described in the text. JEL Classifications: C11; C53; G17; M40. [ABSTRACT FROM AUTHOR]

The term learning styles (LS) describes the notion that individuals have a preferred modality of learning (i.e., vision, audition, or kinesthesis) and that matching instruction to this modality results in optimal learning. During the last decades, LS has received extensive criticism, yet they remain a virtual truism within education. One of the major strands of criticism is the fact that only a handful of studies have systematically put the LS assumptions to the test. In this study, we aimed to explore whether learners who are visual types will be better at learning sign‐words (i.e., ecologically valid stimuli) compared to auditory and kinesthetic types. Ninety‐nine volunteers (67 females, mean age = 28.66 years) naive to Greek Sign Language (GSL) were instructed to learn 20 GSL sign‐words. The volunteers further completed two LS questionnaires (i.e., the Barsch Learning Styles Inventory and the Learning Channels Inventory) and they also reported what their LS they believed was. No evidence of a difference in learning sign‐words among individuals with different LS (as identified by either of the LS questionnaires or by direct self‐report) was found, neither using a frequentist nor using a Bayesian approach to data analysis. Moreover, inconsistencies between the way participants were classified based on the different measures and direct self‐report were detected. These findings add further support to the criticism of the LS theory and its use in educational settings. We suggest that research and practice resources should be allocated to evidence‐based approaches. Τhe notion of LS posits that individuals have a preferred modality of learning and that presenting to‐be‐learned material in this modality results in optimal learning. Here, we report a systematic study testing this notion. The study uses novel and ecologically valid stimuli, namely sign‐words, as the to‐be‐learned material. We hypothesized that individuals with a visual LS will be at an advantage when learning sign‐words, a hypothesis we failed to confirm. [ABSTRACT FROM AUTHOR]

This study was conducted to test the significance of adding row and column factors in the frequentist and Bayesian models used in the evaluation of a population of Passiflora edulis, as well as selecting promising genotypes to form the next generation. The following parameters were evaluated: number of fruits, yield, fruit weight, transverse fruit diameter, longitudinal fruit diameter, pulp percentage, skin thickness and total soluble solids. For the Bayesian model, two priors were considered, namely, inverse gamma and a priori distribution with extended parameters. The model with a priori distribution with extended parameters showed lower root mean square error and higher correlation coef- ficient between observed and predicted values than the inverse gamma model. Furthermore, for a selection intensity of 37%, the mixed and Bayesian models selected practically the same progenies in both experiments. The use of the 5-fold cross-validation technique indicated that both tested models were efficient. [ABSTRACT FROM AUTHOR]

Bayesian data analysis is gaining traction in many fields, including empirical studies in software engineering. Bayesian approaches provide many advantages over traditional, or frequentist, data analysis, but the mechanics often remain opaque to beginners due to the underlying computational complexity. Introductory articles, while successful in explaining the theory and principles, fail to provide a totally transparent operationalization. To address this gap, this tutorial provides a step-by-step illustration of Bayesian hypothesis testing in the context of software engineering research using a fully developed example and in comparison to the frequentist hypothesis testing approach. It shows how Bayesian analysis can help build evidence over time incrementally through a family of experiments. It also discusses chief advantages and disadvantages in an applied manner. A figshare package is provided for reproducing all calculations. [ABSTRACT FROM AUTHOR]

The restricted mean survival time (RMST) evaluates the expectation of survival time truncated by a prespecified time point, because the mean survival time in the presence of censoring is typically not estimable. The frequentist inference procedure for RMST has been widely advocated for comparison of two survival curves, while research from the Bayesian perspective is rather limited. For the RMST of both right‐ and interval‐censored data, we propose Bayesian nonparametric estimation and inference procedures. By assigning a mixture of Dirichlet processes (MDP) prior to the distribution function, we can estimate the posterior distribution of RMST. We also explore another Bayesian nonparametric approach using the Dirichlet process mixture model and make comparisons with the frequentist nonparametric method. Simulation studies demonstrate that the Bayesian nonparametric RMST under diffuse MDP priors leads to robust estimation and under informative priors it can incorporate prior knowledge into the nonparametric estimator. Analysis of real trial examples demonstrates the flexibility and interpretability of the Bayesian nonparametric RMST for both right‐ and interval‐censored data. [ABSTRACT FROM AUTHOR]

Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive RR intervals between hemodynamically stable (HS) and unstable (HU) patients during a hemodialysis session, through linear and nonlinear methods. Forty-six chronic kidney disease patients volunteered in this study. Successive RR intervals and blood pressures were recorded throughout the hemodialysis session. Hemodynamic stability was defined based on the delta of systolic blood pressure (higher SBP-lower SBP). The cutoff for hemodynamic stability was defined as 30 mm Hg, and patients were stratified as: HS ([n = 21]: ≤29.9 mm Hg) or HU ([n = 25]: ≥30 mm Hg). Linear methods (low-frequency [LFnu] and high-frequency [HFnu] spectra) and nonlinear methods (multiscale entropy [MSE] for Scales 1–20, and fuzzy entropy) were applied. The area under the MSE curve at Scales 1–5 (MSE1–5), 6–20 (MSE6–20), and 1–20 (MSE1–20) were also used as nonlinear parameters. Frequentist and Bayesian inferences were applied to compare HS and HU patients. The HS patients exhibited a significantly higher LFnu and lower HFnu. For MSE parameters, Scales 3–20 were significantly higher, as well as MSE1–5, MSE6–20, and MSE1–20 in HS, when compared to HU patients (p < 0.05). Regarding Bayesian inference, the spectral parameters demonstrated an anecdotal (65.9%) posterior probability favoring the alternative hypothesis, while MSE exhibited moderate to very strong probability (79.4 to 96.3%) at Scales 3–20, and MSE1–5, MSE6–20, and MSE1–20. HS patients exhibited a higher heart-rate complexity than HU patients. In addition, the MSE demonstrated a greater potential than spectral methods to differentiate variability patterns in successive RR intervals. [ABSTRACT FROM AUTHOR]

Traditional and modern spatial prediction techniques are applied in the analysis of water quality, evidencing a new approach that allows the modeling of a hydrogeological system in the central area of Boyacá, Colombia. The objective is to determine the quality status of groundwater for human consumption. In the process, spatial predictions were made based on frequentist methods (kriging, cokriging) and Bayesian methods (R-INLA stochastic partial differential equation) as an alternative to Markov chain Monte Carlo methods that require a large computational cost. From the application of these methods, a comparison is made by statistical tests that determine the goodness of adjustment of the predictions. Our interest here is in the implementation of future more robust, economic and scientific solutions, particularly for hydrogeological data, and the proper management of water resources. Finally, the vulnerability of aquifers is analyzed with the DRASTIC method, which takes into account the surrounding media of the aquifers through the variables depth (D), net recharge (R), type of aquifer (A), soil (S), topography (T), impact of the vadose zone (I) and hydraulic conductivity (C). [ABSTRACT FROM AUTHOR]

The Bayesian-frequentist hybrid model and associated inference can combine the advantages of both Bayesian and frequentist methods and avoid their limitations. However, except for few special cases in existing literature, the computation under the hybrid model is generally nontrivial or even unsolvable. This article develops a computation algorithm for hybrid inference under any general loss functions. Three simulation examples demonstrate that hybrid inference can improve upon frequentist inference by incorporating valuable prior information, and also improve Bayesian inference based on non-informative priors where the latter leads to biased estimates for the small sample sizes used in inference. The proposed method is illustrated in applications including a biomechanical engineering design and a surgical treatment of acral lentiginous melanoma. [ABSTRACT FROM AUTHOR]

We propose new ways to compare two latent distributions when only ordinal data are available, and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other. We also show how other ordinal conditions provide evidence of higher within-group inequality in one distribution than in the other, quantified by particular interquantile ranges being wider in one latent distribution than in the other. Second, we propose an 'inner' confidence set for the quantiles that are higher for the first latent distribution. We also describe frequentist and Bayesian inference on features of the ordinal distributions relevant to our identification results. Our contributions are illustrated by empirical examples with mental health and general health. [ABSTRACT FROM AUTHOR]

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders. [ABSTRACT FROM AUTHOR]

Background: Voxel-Based (VB) analysis embraces a multifaceted ensemble of sophisticated techniques, lying at the boundary between image processing and statistical modeling, that allow for a frequentist inference of pathophysiological properties anchored to an anatomical reference. VB methods has been widely adopted in neuroimaging studies and, more recently, they are gaining momentum in radiation oncology research. However, the price for the power of VB analysis is the complexity of the underlying mathematics and algorithms. Purpose: In this paper, we present the Multi-pAradigM voxel-Based Analysis (MAMBA) toolbox, which is intended for a flexible application of VB analysis in a wide variety of scenarios in medical imaging and radiation oncology. Methods: The MAMBA toolbox is implemented in Matlab. It provides opensource functions to compute VB statistical models of the input data,according to a great variety of regression schemes, and to derive VB maps of the observed significance level, performing a non-parametric permutation inference. The toolbox allows for including VB and global outcomes, as well as an arbitrary amount of VB and global Explanatory Variables (EVs). In addition, the Matlab Parallel Computing Toolbox is exploited to take advantage of the perfect parallelizability of most workloads. Results: The use of MAMBA was demonstrated by means of several realistic examples on a synthetic dataset mimicking a radiation oncology scenario. Conclusion: MAMBA is an open-source toolbox, freely available for academic and non-commercial purposes. It is designed to make state-of -the-art VB analysis accessible to research scientists without the programming resources needed to build from scratch their own software solutions. At the same time, the source code is handed out for more experienced users to complement their own tools, also customizing user-defined models. MAMBA guarantees high generality and flexibility in the design of the statistical models, significantly expanding on the features of available free tools for VB analysis. The presented toolbox aims at increasing the reach of VB studies as well as the sharing of research results. [ABSTRACT FROM AUTHOR]

The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach's reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated two approaches for C stock prediction using two new generation multispectral remote sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical paradigms handle uncertainty. Information on C stock spectral prediction in the form of NDVI, SAVI, and EVI derived from multispectral remote sensing platforms, Landsat-8 and Sentinel-2, was used to build Bayesian and frequentist-based C stock predictive models in the sampled plantation forest ecosystem. Sentinel-2-based C stock predictive models outperform their Landsat-8 counterparts using both the Bayesian and frequentist inference approaches. However, the Bayesian-based Sentinel-2 C stock predictive model ( R M S E   =   0.17   M g C h a − 1 ) is more accurate than its frequentist-based Sentinel-2 ( R M S E = 1.19  M g C h a − 1 ) C stock equivalent. The Sentinel-2 frequentist-based C stock predictive model gave the C stock prediction range of  1   ≤   M g C h a − 1   ≤   290 , whilst the Sentinel-2 Bayesian-based C stock predictive model resulted in the prediction range of  1   ≤   M g C h a − 1   ≤   285 . However, both the Bayesian and frequentist C stock predictive models built with the Landsat-8 sensor overpredicted the sampled C stock because the range of predicted values fell outside the range of the observed C stock values. As a result, we recommend and conclude that the Bayesian-based C stock prediction method, when it is combined with high-quality remote sensing data such as that of Sentinel-2, is an effective inferential statistical methodology for reporting C stock in managed plantation forest ecosystems. [ABSTRACT FROM AUTHOR]

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We train and evaluate 136 statistical models with a rainfall dataset with five landslide-triggering rainfall events recorded near Sitka, Alaska, USA, as well as >6,000 days of non-triggering rainfall (2002–2020). We use Akaike, Bayesian, and leave-one-out information criteria to compare models trained on cumulative precipitation at timescales ranging from 1 hour to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as with testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without. Although frequentist and Bayesian inference produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. Three-hour precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale combined with the limited role of antecedent conditions reflects the rapid draining of porous colluvial soils on very steep hillslopes around Sitka. We use the resulting estimates of daily landslide probability to establish two decision boundaries for three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning "dashboard" system (sitkalandslide.org). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers. [ABSTRACT FROM AUTHOR]

Using multi-photon entangled input states, we estimate the phase uncertainty in a noiseless Mach–Zehnder interferometer using photon-counting detection. We assume a flat prior uncertainty and use Bayesian inference to construct a posterior uncertainty. By minimizing the posterior variance to get the optimal input states, we first devise an estimation and measurement strategy that yields the lowest phase uncertainty for a single measurement. N00N and Gaussian states are determined to be optimal in certain regimes. We then generalize to a sequence of repeated measurements, using non-adaptive and fully adaptive measurements. N00N and Gaussian input states are close to optimal in these cases as well, and optimal analytical formulae are developed. Using these formulae as inputs, a general scaling formula is obtained, which shows how many shots it would take on average to reduce phase uncertainty to a target level. Finally, these theoretical results are compared with a Monte Carlo simulation using frequentist inference. In both methods of inference, the local non-adaptive method is shown to be the most effective practical method to reduce phase uncertainty. [ABSTRACT FROM AUTHOR]

Coastal environments are highly threatened by invasive alien plants (IAP), and Remote Sensing (RS) may offer a sound support for IAP detection and mapping. There is still a need for an overview of the progress and extent of RS applications on invaded coasts that can help the development of better RS procedures to support IAP management. We conducted a systematic literature review of 68 research papers implementing, recommending, or discussing RS tools for IAP mapping in coastal environments, published from 2000 to 2021. According to this review, most research was done in China and USA, with Sporobolus (17.3%) being the better studied genus. The number of studies increased at an accelerated rate from 2015 onwards, coinciding with the transition from RS for IAP detection to RS for invasion modeling. The most used platforms in the 2000s were aircraft, with satellites that increased from 2005 and unmanned aerial vehicles after 2014. Frequentist inference was the most adopted classification approach in the 2000s, as machine learning increased after 2009. RS applications vary with coastal ecosystem types and across countries. RS has a huge potential to further improve IAP monitoring. The extension of RS to all coasts of the world requires advanced applications that bring together current and future Earth observation data. [ABSTRACT FROM AUTHOR]

The Jeffreys–Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current literature suggests, the paradox was central to the Bayesian testing methodology developed by Sir Harold Jeffreys in the late 1930s. Jeffreys showed that the evidence for a point-null hypothesis H 0 scales with n and repeatedly argued that it would, therefore, be mistaken to set a threshold for rejecting H 0 at a constant multiple of the standard error. Here, we summarize Jeffreys's early work on the paradox and clarify his reasons for including the n term. The prior distribution is seen to play a crucial role; by implicitly correcting for selection, small parameter values are identified as relatively surprising under H 1 . We highlight the general nature of the paradox by presenting both a fully frequentist and a fully Bayesian version. We also demonstrate that the paradox does not depend on assigning prior mass to a point hypothesis, as is commonly believed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is an oxidative stress-linked neurodegenerative disorder, with the highest prevalence among seniors. The objective of this study were: (1) to analyse levels of following oxidative stress parameters: total antioxidant capacity (TAC), uric acid (UA), total glutathione (tGSH), bilirubin (Bil) and albumin (Alb), in blood of PD patients and healthy controls; (2) to find possible associations of examined oxidative stress parameters with PD subtypes and levodopa treatment status; and (3) to evaluate power and relevance of the aforementioned oxidative stress parameter for the prediction of onset and progression of PD by utilizing Random Forest machine learning (RFML). Oxidative stress parameters were determined in 125 PD patients and 55 healthy controls. Evaluated with frequentist statistics, our data revealed that UA is the only oxidative stress parameter associated with PD. However, when the PD cohort was divided in gender-dependent manner, tGSH and Bil were also significantly associated with PD in subgroup of female patients. RFML rendered no predictive power of any of the tested oxidative stress parameters in respect to PD, its subtypes, and/or status of levodopa treatment. In conclusion, despite the positive association of UA with PD (in complete cohort of PD patients) and of tGSH and Bil with PD but only in female patients, these oxidative stress parameters are of no use in clinical practice due to the lack of the predictive/diagnostic power. [ABSTRACT FROM AUTHOR]

While much of Parts I-III cover basic statistical methods for analyzing normal data, many wildlife population or distribution studies collect count data or binary presence-absence data that do not follow normal distributions. Finally, in Chapter 17 (Models for Zero-Inflated Data), Fieberg shows how to test for zero inflation and account for it because wildlife count data or binary data may sometimes be inflated with more zero observations than expected from a distribution. Fieberg demonstrates stepwise selection using Akaike's Information Criterion, a model selection method that ecology students will likely encounter in graduate school. [Extracted from the article]

Introduction: Quantitative autistic-like traits (QATs) are a constellation of traits that mirror those of clinical autism and are thought to share the same mechanisms as the condition. There is great interest in identifying the genetic and neurobiological basis of QATs, but progress is hindered by the composite nature of these clinically based constructs. Social QATs are defined according to the diagnostic criteria for autism, comprising multiple potential neural mechanisms that may contribute to varying degrees. The objective of this study was to decompose social QATs into more specific constructs, in line with the Research Domain Criteria (RDoC). We chose constructs with trait-like properties and known or suggested significance for autistic social function: (1) social anhedonia, (2) prosopagnosia (face blindness), and (3) mentalizing (attributing mental states to images of eyes). We hypothesized that these constructs may all contribute to observed variance in social QATs. Methods: We recruited 148 adults with a broad range of QATs (mean age 37.9 years, range 18-69; 50% female; 5.4% autistic) to an experimental behavioral study conducted online. We estimated social QATs using the social factor of the Comprehensive Autistic Traits Inventory. We used the Oxford Face Matching Task and the Reading the Mind in the Eyes Test to measure face matching ability and mentalizing, respectively. Social anhedonia traits were measured with the Anticipatory and Consummatory Interpersonal Pleasure Scale, and prosopagnosic traits with the 20-item Prosopagnosia Index. A combination of frequentist and Bayesian statistics was used to test the social constructs as predictors of social QATs. Results: We found that social anhedonic traits, prosopagnosic traits, and face matching performance were likely predictors of social QATs, whereas mentalizing showed limited contribution. Conclusion: The findings support prosopagnosic and anhedonic traits, but not mentalizing deficits, as dimensional predictors of individual differences in social function across the autistic spectrum. Further, the study strongly suggests that social reward systems and face processing networks play significant and independent roles in autistic-like social function. [ABSTRACT FROM AUTHOR]

Background: Availability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based analyses. Among these, Bayesian networks have great potential to tackle complex interdisciplinary problems, because they can easily model inter-relations between variables. They work by encoding conditional independence relationships discovered via advanced inference algorithms. One challenge is dealing with missing data, ubiquitous in survey or biomedical datasets. Missing data is rarely addressed in an advanced way in Bayesian networks; the most common approach is to discard all samples containing missing measurements. This can lead to biased estimates. Here, we examine how Bayesian network structure learning can incorporate missing data. Methods: We use a simulation approach to compare a commonly used method in frequentist statistics, multiple imputation by chained equations (MICE), with one specific for Bayesian network learning, structural expectation-maximization (SEM). We simulate multiple incomplete categorical (discrete) data sets with different missingness mechanisms, variable numbers, data amount, and missingness proportions. We evaluate performance of MICE and SEM in capturing network structure. We then apply SEM combined with community analysis to a real-world dataset of linked biomedical and social data to investigate associations between socio-demographic factors and multiple chronic conditions in the US elderly population. Results: We find that applying either method (MICE or SEM) provides better structure recovery than doing nothing, and SEM in general outperforms MICE. This finding is robust across missingness mechanisms, variable numbers, data amount and missingness proportions. We also find that imputed data from SEM is more accurate than from MICE. Our real-world application recovers known inter-relationships among socio-demographic factors and common multimorbidities. This network analysis also highlights potential areas of investigation, such as links between cancer and cognitive impairment and disconnect between self-assessed memory decline and standard cognitive impairment measurement. Conclusion: Our simulation results suggest taking advantage of the additional information provided by network structure during SEM improves the performance of Bayesian networks; this might be especially useful for social science and other interdisciplinary analyses. Our case study show that comorbidities of different diseases interact with each other and are closely associated with socio-demographic factors. [ABSTRACT FROM AUTHOR]

Linear mixed models are standard models to analyze repeated measures or longitudinal data under the assumption of normality for random components in the model. Although the mixed models are often used in both frequentist and Bayesian inference, their evaluation from robustness perspective has not received as much attention in Bayesian inference as in frequentist. The aim of this study is to evaluate Bayesian tests in mixed models for their robustness to normality. We use a general class of exponential power distributions, EPD, and particularly focus on testing fixed effects in longitudinal models. The EPD class contains both light and heavy tailed distributions, with normality as a special case. Further, we consider a new paradigm of Bayesian testing decision theory where the hypotheses are formulated as a mixture model, with subsequent testing based on the posterior distribution of the mixture weights. It is shown that the EPD class provides a flexible alternative to normality assumption, particularly in the presence of outliers. Real data applications are also demonstrated. [ABSTRACT FROM AUTHOR]