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When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences. We propose that a more suitable default choice is the normal distribution with mean zero and standard deviation equal to the standard error of the M-estimator. We base this recommendation on two arguments. First, we show that this prior is non-informative for inference about the sign of the regression coefficient. Second, we show that this prior agrees well with a meta-analysis of 50 articles from the MEDLINE database. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new distribution for positively skewed data by combining the Birnbaum-Saunders and centered skew-normal distributions. Several of its properties are developed. Our model accommodates both positively and negatively skewed data. Also, we show that our proposal circumvents some problems related to another Birnbaum-Saunders distribution based on the usual skew-normal model, previously presented in the literature. We derive both maximum likelihood and Bayesian inference, comparing them through a suitable simulation study. The convergence of the expectation conditional maximization (for maximum likelihood inference) and MCMC algorithms (for Bayesian inference) are verified and several factors of interest are compared. In general, as the sample size increases, the results indicate that the Bayesian approach provided the most accurate estimates. Our model accommodates the asymmetry of the data more properly than the usual Birnbaum-Saunders distribution, which is illustrated through real data analysis. [ABSTRACT FROM AUTHOR]

Radioactivity detection is a major research and development priority for many practical applications. Amongst the various technical challenges in this field is the need to carry out accurate low-level radioactivity measurements in the presence of a large fluctuations in the natural radiation background, while reducing the false alarm rates. The task becomes even more harder with high detection limits under low signal-to-background ratios. A detection method based on the statistical inference, following either a frequentist or a Bayesian paradigm, adopted to overcome these challenges as well as to ensure a reliable and accurate diagnosis with a competitive tradeoff between sensitivity, specificity and response time. With this respect, several research studies, addressing a range of applications from decommissioning and dismantling to homeland security, have been proposed. Our main goal in this paper is to present a succinct survey of these studies based on a frequentist and Bayesian approaches used to decision-making, uncertainty and risk evaluation, in the context of radioactive detection. In this prospect, a theoretical background of statistical frequentist and Bayesian inferences was presented. Then, a comparative study of both approaches was performed to determine the optimal approach in regards to accuracy and pros/cons. A case of study for low-level radioactivity detection in nuclear decommissioning operations was provided to validate the optimal approach. Results proved the efficiency and usefulness of Bayesian approach against frequentist one with respect to the most challenging scenarios in radiation detection applications. • A detection method based on the statistical inference is adopted for detecting a low level of radioactivity in the presence of a large fluctuations in the natural radiation background and under low signal-to-background ratios. • A brief overview of frequentist and Bayesian paradigm-based studies of decision making, uncertainty and risk assessment, in the context of radioactive detection. • Bayesian approach has demonstrated its effectiveness and utility compared to the frequentist approach, particularly in the most challenging scenarios encountered in radiation detection applications. [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., (© 2023 The Authors. Clinical Psychology & Psychotherapy published by John Wiley & Sons Ltd.)

Near-death experiences (NDEs) are usually associated with positive affect, however, a small proportion are considered distressing. We aimed to look into the proportion of distressing NDEs in a sample of NDE narratives, categorise distressing narratives according to Greyson and Bush's classification (inverse, void or hellish), and compare distressing and "classical" NDEs. Participants wrote down their experience, completed the Memory Characteristics Questionnaire (assessing the phenomenology of memories) and the Greyson scale (characterising content of NDEs). The proportion of suicidal attempts, content and intensity of distressing and classical NDEs were compared using frequentist and Bayesian statistics. Distressing NDEs represent 14% of our sample (n = 123). We identified 8 inverse, 8 hellish and 1 void accounts. The proportion of suicide survivors is higher in distressing NDEs as compared to classical ones. Finally, memories of distressing NDEs appear as phenomenologically detailed as classical ones. Distressing NDEs deserve careful consideration to ensure their integration into experiencers' identity. [ABSTRACT FROM AUTHOR]

Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a 'consumer's guide' to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient 'reference' prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new 'default' prior for any Bayesian model. [ABSTRACT FROM AUTHOR]

Background: Recurrent Respiratory Papillomatosis (RRP) is a rare disease characterized by the growth of papillomas in the airway and especially the larynx. The clinical course is highly variable among individuals and there is poor understanding of the factors that drive an aggressive vs an indolent course. Methods: A convenience cohort of 339 affected subjects with papillomas positive for only HPV6 or HPV11 and clinical course data available for 1 year or more, from a large multicenter international study were included. Exploratory data analysis was conducted followed by inferential analyses with frequentist and Bayesian statistics. Results: We examined 339 subjects: 82% were diagnosed prior to the age of 18 years, 65% were infected with HPV6, and 69% had an aggressive clinical course. When comparing age at diagnosis with clinical course, the probability of aggressiveness is high for children under five years of age then drops rapidly. For patients diagnosed after the age of 10 years, an indolent course is more common. After accounting for confounding between HPV11 and young age, HPV type was minimally associated with aggressiveness. Fast and Frugal Trees (FFTs) were utilized to determine which algorithms yield the highest accuracy to classify patients as having an indolent or aggressive clinical course and consistently created a branch for diagnostic age at ~5 years old. There was no reliable strong association between clinical course and socioeconomic or parental factors. Conclusion: In the largest cohort of its type, we have identified a critical age at diagnosis which demarcates a more aggressive from less aggressive clinical course. [ABSTRACT FROM AUTHOR]

This paper develops a frequentist model averaging approach for threshold model specifications. The resulting estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. In particular, when combining estimators from threshold autoregressive models, this approach is also proved to be asymptotically optimal. Simulation results show that for the situation where the existing model averaging approach is not applicable, our proposed model averaging approach has a good performance; for the other situations, our proposed model averaging approach performs marginally better than other commonly used model selection and model averaging methods. An empirical application of our approach on the US unemployment data is given. [ABSTRACT FROM AUTHOR]

When data analysts operate within different statistical frameworks (e.g., frequentist versus Bayesian, emphasis on estimation versus emphasis on testing), how does this impact the qualitative conclusions that are drawn for real data? To study this question empirically we selected from the literature two simple scenarios—involving a comparison of two proportions and a Pearson correlation—and asked four teams of statisticians to provide a concise analysis and a qualitative interpretation of the outcome. The results showed considerable overall agreement; nevertheless, this agreement did not appear to diminish the intensity of the subsequent debate over which statistical framework is more appropriate to address the questions at hand. [ABSTRACT FROM AUTHOR]

This article resulted from our participation in the session on the "role of expert opinion and judgment in statistical inference" at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. We close with a call for increased proactivity and involvement of statisticians in study conceptualization, design, conduct, analysis, and communication. [ABSTRACT FROM AUTHOR]

A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald's development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts. [ABSTRACT FROM AUTHOR]

Both philosophically and in practice, statistics is dominated by frequentist and Bayesian thinking. Under those paradigms, our courses and textbooks talk about the accuracy with which true model parameters are estimated or the posterior probability that they lie in a given set. In nonparametric problems, they talk about convergence to the true function (density, regression, etc.) or the probability that the true function lies in a given set. But the usual paradigms' focus on learning the true model and parameters can distract the analyst from another important task: discovering whether there are many sets of models and parameters that describe the data reasonably well. When we discover many good models we can see in what ways they agree. Points of agreement give us more confidence in our inferences, but points of disagreement give us less. Further, the usual paradigms' focus seduces us into judging and adopting procedures according to how well they learn the true values. An alternative is to judge models and parameter values, not procedures, and judge them by how well they describe data, not how close they come to the truth. The latter is especially appealing in problems without a true model. [ABSTRACT FROM AUTHOR]

Highlights • Discussion are provided on how groundwater numerical models and data interact. • Frequentist and Bayesian methods are reviewed and classified. • Eight application areas of groundwater model-data interaction are reviewed. • Discussions on the state of the art in software and codes are presented. • Recent trends and issues that will likely form future directions are discussed. Abstract We define model-data interaction (MDI) as a two way process between models and data, in which on one hand data can serve the modeling purpose by supporting model discrimination, parameter refinement, uncertainty analysis, etc., and on the other hand models provide a tool for data fusion, interpretation, interpolation, etc. MDI has many applications in the realm of groundwater and has been the topic of extensive research in the groundwater community for the past several decades. This has led to the development of a multitude of increasingly sophisticated methods. The progress of data acquisition technologies and the evolution of models are continuously changing the landscape of groundwater MDI, creating new challenges and opportunities that must be properly understood and addressed. This paper aims to review, analyze and classify research on MDI in groundwater applications, and discusses several related aspects including: (1) basic theoretical concepts and classification of methods, (2) sources of uncertainty and how they are commonly addressed, (3) specific characteristics of groundwater models and data that affect the choice of methods, (4) how models and data can interact to provide added value in groundwater applications, (5) software and codes for MDI, and (6) key issues that will likely form future research directions. The review shows that there are many tools and techniques for groundwater MDI, and this diversity is needed to support different MDI objectives, assumptions, model and data types and computational constraints. The study identifies eight categories of applications for MDI in the groundwater literature, and highlights the growing gap between MDI practices in the research community and those in consulting, industry and government. [ABSTRACT FROM AUTHOR]

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results. [ABSTRACT FROM AUTHOR]

Frequentist methods have long dominated data analysis in quantitative second language (L2) research. Recently, however, several empirical fields have begun to embrace alternatives known as Bayesian methods. Using an open‐source approach, we provide an applied, nontechnical rationale for Bayesian methods in L2 research. First, we compare the conceptual underpinning of Bayesian and frequentist methods. Second, using real as well as carefully simulated examples, we introduce and apply Bayesian methods to various research designs. Third, to promote the use of Bayesian methods in L2 research, we introduce a free Web‐accessed point‐and‐click software package (https://rnorouzian.shinyapps.io/bayesian-t-tests) as well as a suite of flexible R functions developed by the first author. Additionally, we demonstrate Bayesian methods for conducting secondary analysis on previously published literature. Finally, we discuss practical and theoretical dimensions of a Bayesian revolution in L2 research. Open Practices: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/jxd47, the IRIS Repository at https://www.iris-database.org, and GitHub at https://github.com/rnorouzian/i/blob/master/i.r. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [ABSTRACT FROM AUTHOR]

Nutrient pollution such as nitrate (NO3−) can cause water quality degradation in rivers used as a source of drinking water. This situation raises the question of how the nutrients have moved depending on many factors such as land use and anthropogenic sources. Researchers developed several nutrient export coefficient models depending on the aforementioned factors. To this purpose, statistical data including a number of factors such as historical water quality and land use data for the Melen Watershed were used. Nitrate export coefficients are estimates of the total load or mass of nitrate (NO3−) exported from a watershed standardized to unit area and unit time (e.g. kg/km2/day). In this study, nitrate export coefficients for the Melen Watershed were determined using the model that covers the Frequentist and Bayesian approaches. River retention coefficient was determined and introduced into the model as an important variable.

The debate between Bayesians and frequentist statisticians has been going on for decades. Whilst there are fundamental theoretical and philosophical differences between both schools of thought, we argue that in two most common situations the practical differences are negligible when off-the-shelf Bayesian analysis (i.e., using ‘objective’ priors) is used. We emphasize this reasoning by focusing on interval estimates: confidence intervals and credible intervals. We show that this is the case for the most common empirical situations in the social sciences, the estimation of a proportion of a binomial distribution and the estimation of the mean of a unimodal distribution. Numerical differences between both approaches are small, sometimes even smaller than those between two competing frequentist or two competing Bayesian approaches. We outline the ramifications of this for scientific practice.

The theory of financial markets is well developed, but before any of it can be applied there are statistical questions to be answered: Are the hypotheses of proposed models reasonably consistent with what data show? If so, how should we infer parameter values from data? How do we quantify the error in our conclusions? This paper examines these questions in the context of the two main areas of quantitative finance, portfolio selection and derivative pricing. By looking at these two contexts, we get a very clear understanding of the viability of the two main statistical paradigms, classical (frequentist) statistics and Bayesian statistics. [ABSTRACT FROM AUTHOR]

In this paper a novel approach is presented for history matching models without making assumptions about the measurement error. Interval Predictor Models are used to robustly model the observed data and hence a novel figure of merit is proposed to quantify the quality of matches in a frequentist probabilistic framework. The proposed method yields bounds on the p -values from frequentist inference. The method is first applied to a simple example and then to a realistic case study (the Imperial College Fault Model) in order to evaluate its applicability and efficacy. When there is no modelling error the method identifies a feasible region for the matched parameters, which for our test case contained the truth case. When attempting to match one model to data from a different model, a region close to the truth case was identified. The effect of increasing the number of data points on the history matching is also discussed. [ABSTRACT FROM AUTHOR]

A new general class of exponentiated sinh Cauchy regression models for location, scale, and shape parameters is introduced and studied. It may be applied to censored data and used more effectively in survival analysis when compared with the usual models. For censored data, we employ a frequentist analysis for the parameters of the proposed model. Further, for different parameter settings, sample sizes, and censoring percentages, various simulations are performed. The extended regression model is very useful for the analysis of real data and could give more adequate fits than other special regression models. [ABSTRACT FROM AUTHOR]

Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks. [ABSTRACT FROM AUTHOR]

Just as frequentist hypothesis tests have been developed to check model assumptions, prior predictive p-values and other Bayesian p-values check prior distributions as well as other model assumptions. These model checks not only suffer from the usual threshold dependence of p-values, but also from the suppression of model uncertainty in subsequent inference. One solution is to transform Bayesian and frequentist p-values for model assessment into a fiducial distribution across the models. Averaging the Bayesian or frequentist posterior distributions with respect to the fiducial distribution can reproduce results from Bayesian model averaging or classical fiducial inference. [ABSTRACT FROM AUTHOR]

Abstract: Frequentist model averaging has been proposed as a method for incorporating “model uncertainty” into confidence interval construction. Such proposals have been of particular interest in the environmental and ecological statistics communities. A promising method of this type is the model averaged tail area (MATA) confidence interval put forward by Turek & Fletcher (2012). The performance of this interval depends greatly on the data‐based model weights. A computationally convenient formula for the coverage probability of this interval was provided by Kabaila, Welsh, & Abeysekera (2016), in the simple scenario of two nested linear regression models. We consider more complicated scenarios with a large number of linear regression models. For each of a given set of components of the regression parameter vector, we either set the component to zero or let it vary freely. We provide an easily computed upper bound on the minimum coverage probability of the MATA confidence interval. This upper bound provides evidence against the use of a model weight based on the Bayesian Information Criterion (BIC). The Canadian Journal of Statistics 46: 279–297; 2018 © 2018 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Statistical inference is a widely-used, powerful tool for learning about natural processes in diverse fields. The statistical software platforms AD Model Builder (ADMB) and Template Model Builder (TMB) are particularly popular in the ecological literature, where they are typically used to perform frequentist inference of complex models. However, both lack capabilities for flexible and efficient Markov chain Monte Carlo (MCMC) integration. Recently, the no-U-turn sampler (NUTS) MCMC algorithm has gained popularity for Bayesian inference through the software Stan because it is efficient for high dimensional, complex hierarchical models. Here, we introduce the R packages and , which provide NUTS sampling in parallel and interactive diagnostics with ShinyStan. The ADMB source code was modified to provide NUTS, while TMB models are linked directly into Stan. We describe the packages, provide case studies demonstrating their use, and contrast performance against Stan. For TMB models, we show how to test the accuracy of the Laplace approximation using NUTS. For complex models, the performance of ADMB and TMB was typically within +/- 50% the speed of Stan. In one TMB case study we found inaccuracies in the Laplace approximation, potentially leading to biased inference. provides a new method for estimating hierarchical ADMB models which previously were infeasible. TMB users can fit the same model in both frequentist and Bayesian paradigms, including using NUTS to test the validity of the Laplace approximation of the marginal likelihood for arbitrary subsets of parameters. These software developments extend the available statistical methods of the ADMB and TMB user base with no additional effort by the user. [ABSTRACT FROM AUTHOR]

Trials run in either rare diseases, such as rare cancers, or rare sub-populations of common diseases are challenging in terms of identifying, recruiting and treating sufficient patients in a sensible period. Treatments for rare diseases are often designed for other disease areas and then later proposed as possible treatments for the rare disease after initial phase I testing is complete. To ensure the trial is in the best interests of the patient participants, frequent interim analyses are needed to force the trial to stop promptly if the treatment is futile or toxic. These non-definitive phase II trials should also be stopped for efficacy to accelerate research progress if the treatment proves to be particularly promising. In this paper, we review frequentist and Bayesian methods that have been adapted to incorporate two binary endpoints and frequent interim analyses. The Eurosarc Trial of Linsitinib in advanced Ewing Sarcoma (LINES) is used as a motivating example and provides a suitable platform to compare these approaches. The Bayesian approach provides greater design flexibility, but does not provide additional value over the frequentist approaches in a single trial setting when the prior is non-informative. However, Bayesian designs are able to borrow from any previous experience, using prior information to improve efficiency. [ABSTRACT FROM AUTHOR]

Conventional methods for testing the mean vector of a P -variate Gaussian distribution require a sample size N greater than or equal to P . But, in high dimensional situations, that is when N is smaller than P , special and new adjustments are needed. Although Bayesian-empirical methods are well-succeeded for testing in high dimension, their performances are strongly dependent on the actual unknown covariance matrix of the Gaussian random vector. In this paper, we introduce a hybrid frequentist–Bayesian Monte Carlo test and prove that: (i) under the null hypothesis, the performance of the proposed test is invariant with respect to the real unknown covariance matrix, and (ii) the decision rule is valid, which means that, in terms of expected loss, the performance of the proposed procedure can always be made as good as the exact Bayesian test and, in terms of type I error probability, the method is always of α level for arbitrary α ∈ ( 0 , 1 ) . [ABSTRACT FROM AUTHOR]

In this paper, we present aWeibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-log) can be obtained as limiting cases. We further compare the proposed model with some other asymmetrical models. The Bayesian as well as frequentist estimation procedures for binomial and multinomial data responses are presented in detail. The analysis of two datasets to show the efficiency of the proposed model is performed. [ABSTRACT FROM AUTHOR]

Normative legal argument based on empirical evidence is not necessarily best served by standards from the social sciences. Precautionary concern for false negatives may call for an adjustment of the significance level. That all legal choice is historically contingent, that legal problems tend to be ill-defined, and that strategic actors have an incentive to bias the generation of evidence create further challenges. Yet the law can capitalize on the adversarial principle. Competition among interested parties helps contain the strategic element and spurs the creative search for better evidence. This leads to suggestive, but institutionally contained, empirical evidence. [ABSTRACT FROM AUTHOR]

In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion. [ABSTRACT FROM AUTHOR]

Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population at the end of the trial. This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error. [ABSTRACT FROM AUTHOR]

Purpose In this paper, the authors aim to introduce the notion of region of origin effect and articulate why home region boundaries should be factored in when understanding firm strategy and outcomes.Design/methodology/approach The paper validates the region of origin effect on internationalization using a sample of 11,677 firms from 99 developing countries in a multilevel model, with both frequentist and Bayesian approaches.Findings The findings of this paper indicate consistent support for the notion of region of origin effect. The relative importance of direct region effects in explaining variation in firm internationalization was found to be 17.8 per cent. When indirect effects (i.e. varying slopes) were factored in, the relative importance was 16.6 per cent. Additionally, the findings show that the region of origin effect impacts the degree of strength of the well-established firm drivers of internationalization.Originality/value Although the importance of the home region location is well known to researchers and practitioners of international business, it has not received the attention it deserves. The findings of this paper clearly demonstrate the need for researchers and practitioners to recognize the role of the region of origin effect in formulating and implementing global strategies. [ABSTRACT FROM AUTHOR]

For unconscious perception research, Bayesian statistics are more appropriate for assessing null awareness of masked stimuli than traditional (frequentist) statistics. This assertion is based mostly upon the theoretical features of Bayesian statistics and modeling studies. To further assess the potential advantages, we compared frequentist and Bayesian statistical tests in a masked Stroop priming experiment in which the prime stimuli were presented at varying degrees of visibility. A novel contribution was to compare a null awareness dissociation approach (i.e., stimulus awareness = 0) to a relative sensitivity approach (indirect or priming effects > direct effects) for the same data. From a null awareness perspective, the frequentist t-tests for the Stroop effect (i.e., perception) for the briefest display conditions had non-significant outcomes. Similar Bayesian t-tests were inconclusive. In contrast, the relative sensitivity dissociation approach was more interpretable, with strong evidence against unconscious perception from a single Bayesian t test. For the longer display conditions, both statistical approaches suggested large conscious perception effects. We conclude that the utility of Bayesian statistics is highly dependent upon the type of dissociation approach, with a relative sensitivity approach being more straightforward to interpret than a null awareness approach. [ABSTRACT FROM AUTHOR]

Wasserstein and Lazar (2016) start with a constructive discussion about the uses and abuses of I p i -values before moving against them. Keywords: data analysis; p-value; statistical inference EN data analysis p-value statistical inference 1 2 2 10/04/22 20221001 NES 221001 There is a need for articles, such as the recent I Conservation Biology i editorial by Mayo (2022), elaborating on and contextualizing the American Statistical Association President's Task Force statement on statistical significance (Benjamini et al., 2021). For such studies, formal statistical methods, such as I p i -values, may not be required, though I p i -values may be still useful. [Extracted from the article]

The article discusses the manipulation of tonal hierarchy using bad feedback. Topics include the experiment suggest that representation of the tonal hierarchy is stable; a linear model predicting confidence with block, scale degree and their interaction as predictors; and using reversed performance feedback to alter the perception of a single note.