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In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$ -statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings. [ABSTRACT FROM AUTHOR]

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is regarded as one of the most common forms of cancer and has recorded high mortality globally. However, lower mortality rates are reported in advanced countries due to early PCa detection and treatment. In developing countries such as Ghana, most PCa victims are diagnosed in the late stage of the disease with poor prognosis. Using a quantitative approach, this study assessed PCa awareness, knowledge, and screening practices considering 423 men aged at least 30 years. We found that the majority of respondents (85.8%) were aware of PCa. About half (52.5%) of the respondents interviewed were deemed to have adequate knowledge of PCa considering our at least 4/8 criterion. Highest educational level and occupation of respondents were found to be significantly associated with awareness and knowledge of PCa. Only, 10.2% of the respondents had ever screened for PCa. Participants with adequate knowledge on PCa had 4.99 times (95% CI = 1.78–13.97) the odds of screening for PCa compared to those with inadequate knowledge. Lack of education on PCa and fear of the unknown were the most reported barriers to prostate cancer screening. There is the need for deliberate and well-tailored dissemination of male reproductive health information regarding PCa from formal sources such as health professionals most especially health educators and promotors through the mass media. [ABSTRACT FROM AUTHOR]

BACKGROUND: Multifidus is an important lumbar muscle with distinct superficial and deep fibers responsible for torque production and stabilization, respectively. Its mechanical properties change when transitioning from lying to sitting positions, necessitating enhanced stability. It holds crucial clinical relevance to assess these layers separately, especially in the sitting posture, which demands increased neuromuscular control compared to the prone position. OBJECTIVE: To compare lumbar multifidus stiffness in lying versus sitting postures, analyzing both superficial and deep layers. METHODS: Supersonic Shear Imaging captured elastographic images from 26 asymptomatic volunteers in prone and seated positions. RESULTS: Left multifidus shear modulus in lying: 5.98 ± 1.80/7.96 ± 1.59 kPa (deep/superficial) and sitting: 12.58 ± 4.22/16.04 ± 6.65 kPa. Right side lying: 6.08 ± 1.97/7.80 ± 1.76 kPa and sitting: 13.25 ± 4.61/17.95 ± 7.12 kPa. No side differences (lying p = 0.99, sitting p = 0.43). However, significant inter-postural differences occurred. CONCLUSION: Lumbar multifidus exhibits increased stiffness in sitting, both layers affected, with superior stiffness in superficial versus deep fibers. Applying these findings could enhance assessing multifidus stiffness changes, for classifying tension-induced low back pain stages. [ABSTRACT FROM AUTHOR]

A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior. [ABSTRACT FROM AUTHOR]

This study investigates the statistical inference of the parameters, reliability function, and hazard function of the generalized Rayleigh distribution under progressive first-failure censoring samples, considering factors such as long product lifetime and challenging experimental conditions. Firstly, the progressive first-failure model is introduced, and the maximum likelihood estimation for the parameters, reliability function, and hazard function under this model are discussed. For interval estimation, confidence intervals have been constructed for the parameters, reliability function, and hazard function using the bootstrap method. Next, in Bayesian estimation, considering informative priors and non-information priors, the Bayesian estimation of the parameters, reliability function, and hazard function under symmetric and asymmetric loss functions is obtained using the MCMC method. Finally, Monte Carlo simulation is conducted to compare mean square errors, evaluating the superiority of the maximum likelihood estimation and Bayesian estimation under different loss functions. The performance of the estimation methods used in the study is illustrated through illustrative examples. The results indicate that Bayesian estimation outperforms maximum likelihood estimation. [ABSTRACT FROM AUTHOR]

The present study was conducted with the aim of investigating the role of attachment styles and emotional selfawareness in explaining the moral intelligence of adolescents. The research method was correlational. The statistical population of this research consists of first-year high school students who were studying in the academic year 2019-2020. A total of 200 students were selected using available sampling methods. Research tools included the emotional self-awareness questionnaire (Grant et al., 2002); attachment style (Hazan & Shaver, 1987); and moral intelligence (Lenink & Keel, 2011). Questionnaires were provided to students online. The collected data were analyzed using descriptive statistics (mean & standard deviation) and inferential statistics (Spearman's correlation) with the help of SPSS software version 22. The results of the research showed that the variables of avoidant attachment style (β = -0.195, p < 0.001), secure attachment style (β = 0.407, p<0.001), and the total score of emotional self-awareness (β = 0.438, p>0.033) had an effective and significant role in explaining moral intelligence and other research variables did not have an effective and significant role. [ABSTRACT FROM AUTHOR]

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) provides support for various mathematics-related subjects, with statistics students being the second-largest cohort of visitors. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support; the categorization of statistical concepts has been made with the statistics module description for each statistics subject at the Universidad Rey Juan Carlos (URJC). First, we conducted a categorization of statistical concepts taught in university (based on URJC's catergorization); after that, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. The statistical issues presented were categorized as descriptive statistics (22.8%), probability (44%), statistical inference (29.2%), and statistical software (4%). Students struggled with material that was introduced at university level rather than material seen at secondary level. Our findings on students' main statistical issues contribute to the development of a suite of evidence-based educational applications and games to support undergraduate students internationally in first- and second-year statistical modules. [ABSTRACT FROM AUTHOR]

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers. [ABSTRACT FROM AUTHOR]

Relative belief inferences are shown to arise as Bayes rules or limiting Bayes rules. These inferences are invariant under reparameterizations and possess a number of optimal properties. In particular, relative belief inferences are based on a direct measure of statistical evidence. [ABSTRACT FROM AUTHOR]

Functional neuroimaging analysis takes noisy multidimensional measurements as input and produces statistical inferences regarding the functional properties of brain regions as output. Such inferences are most commonly model-based, in that they assume a model of how neural activity translates to the measured signal (blood oxygenation level-dependent signal in the case of functional MRI). The use of models increases statistical sensitivity and makes it possible to ask fine-grained theoretical questions. However, this comes at the cost of making theoretical assumptions about the underlying data-generating process. An advantage of model-free approaches is that they can be used in cases where model assumptions are known not to hold. To this end, we introduce a randomization-based, model-free approach to functional neuroimaging. TWISTER randomization makes it possible to infer functional selectivity from correlations between experimental runs. We provide a proof of concept in the form of a visuomotor mapping experiment and discuss the possible strengths and limitations of this new approach in light of our empirical results. [ABSTRACT FROM AUTHOR]

Background: Physiotherapists' attitudes and beliefs play a crucial role in the management and treatment outcomes of non‐specific neck pain (NSNP). Objective: To investigate the attitudes and beliefs of physiotherapists in Jordan towards NSNP, and to identify factors influencing these attitudes and beliefs. Methods: A Cross‐sectional survey of registered physiotherapists working in Jordan was conducted. A structured questionnaire was used to assess the attitudes and beliefs of physiotherapists towards NSNP. Descriptive and inferential statistics were used. Results: A total of 301 PTs completed the survey, 155 females (51.56%). Most respondents held bachelor's degrees, with an average of 9.5 ± 6.6 experience years. The total score for PABS‐PT (Z = 11.4, p < 0.001) and its BM (Z = +11.9, p < 0.001) and PS (Z = 3.6, p < 0. 001) subscales were significantly positive. The score of the biomedical subscale was more significantly positive than psychosocial subscale. The treatment orientation was positively correlated with education level, and negatively with age and experience. The most reported treatments were manual therapy followed by general exercises and electrical modalities. No significant relationship was observed between treatment orientation and treatment selection. Conclusion: This study created the initial profile for pain attitudes and beliefs in Jordan. Physiotherapists hold positive attitudes and beliefs towards the NSNP treatment orientations with a superiority of the BM approach. Engaging educational sectors and stakeholders in the current situation of PTs' attitudes and beliefs is important. [ABSTRACT FROM AUTHOR]

Objective: The COVID-19 pandemic had a profound impact on the mental health of individuals with serious mental illness, with restricting social gatherings and limiting access to essential community and psychosocial support services. For programs like clubhouses, adapting typically in-person programming to online settings led to the creation of virtual clubhouse programming that persists at many sites even after reopening. Although it has been documented how clubhouses adapted their programming online, it has not been investigated at the individual level how those programs were utilized over time, by different member cohorts, and how they persist in comparison to one another. Method: The present article presents descriptive and inferential statistics, analysis of variance, and secondary trend analysis of the Fountain House clubhouse in-person and virtual engagements of three member cohorts who enrolled in either three time periods before pandemic restrictions (the prior cohort), during pandemic restrictions (the pandemic cohort), and after lockdown restrictions (the reopening cohort). Result: Initial findings show that the prior cohort sustained their overall rate of engagement across time periods. The pandemic cohort had a significantly higher rate of engagement than the prior cohort within the during period but demonstrated a significant decrease in engagement rate between the during and after period. Prior and pandemic cohorts had statistically similar virtual and in-person engagement ratios in the after period, but the reopen cohort differed significantly with a predominant ratio of in-person engagements. Conclusions and Implications for Practice: Member engagement trends within in-person and virtual offerings across the three different pandemic related time periods indicate important considerations for the sustainability and innovation of clubhouse virtual programming. Impact and Implications: How clubhouses adapted their programming virtually during pandemic restrictions has been well documented; however, the trends and frequencies of member engagement patterns before, during, and after pandemic restrictions have not been investigated. The present study offers a preliminary analysis of these engagement trends, identifying persistent and robust virtual engagement for members that started prior to and during the pandemic restrictions. Initial findings demonstrate the persistence of virtual engagement for some member cohorts, even when in-person programming is simultaneously available. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme is a highly aggressive form of brain cancer, with a median survival time for diagnosed patients of 15 months. Treatment of this cancer is typically a combination of radiation, chemotherapy and surgical removal of the tumour. However, the highly invasive and diffuse nature of glioblastoma makes surgical intrusions difficult, and the diffusive properties of glioblastoma are poorly understood. In this paper, we introduce a stochastic interacting particle system as a model of in vitro glioblastoma migration, along with a maximum likelihood-algorithm designed for inference using microscopy imaging data. The inference method is evaluated on in silico simulation of cancer cell migration, and then applied to a real data set. We find that the inference method performs with a high degree of accuracy on the in silico data, and achieve promising results given the in vitro data set. [ABSTRACT FROM AUTHOR]

Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalization constants requires summation over large or possibly infinite sets, which can be impractical. This article addresses this computational challenge through the development of a novel generalized Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to update beliefs about model parameters using a discrete Fisher divergence, in lieu of the problematic intractable likelihood. The result is a generalized posterior that can be sampled from using standard computational tools, such as Markov chain Monte Carlo, circumventing the intractable normalizing constant. The statistical properties of the generalized posterior are analyzed, with sufficient conditions for posterior consistency and asymptotic normality established. In addition, a novel and general approach to calibration of generalized posteriors is proposed. Applications are presented on lattice models for discrete spatial data and on multivariate models for count data, where in each case the methodology facilitates generalized Bayesian inference at low computational cost. for this article are available online. [ABSTRACT FROM AUTHOR]

Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires the computation of a sum over all pairs of variables and all observations, which may be prohibitive in high dimensions. Moreover, it may be difficult to calculate confidence intervals of the resulting estimators, as they involve summing all pairs of pairs and all of the four-dimensional marginals. To alleviate these issues, we consider a randomized pairwise likelihood approach, where only summands randomly sampled across observations and pairs are used for the estimation. In addition to the usual tradeoff between statistical and computational efficiency, it is shown that, under a condition on the sampling parameter, this two-way random sampling mechanism makes the individual bivariate likelihood scores become asymptotically independent, allowing more computationally efficient confidence intervals to be constructed. The proposed approach is illustrated in tandem with copula-based models for multivariate count data in simulations, and in real data from a transcriptome study. for this article are available online. [ABSTRACT FROM AUTHOR]

Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifiers may not be perfectly accurate. The impact of diagnostic misclassification in statistical inference has been recently investigated in parametric model contexts and shown to introduce severe bias in estimating treatment effects and give grossly inaccurate inferences. The article aims to address these problems in a fully nonparametric setting. Methods for consistently estimating and testing meaningful yet nonparametric treatment effects are developed. Along the way, we also construct estimators for misclassification error rates and investigate their asymptotic properties. The proposed methods are applicable for outcomes measured in ordinal, discrete, or continuous scales. They do not require any assumptions, such as the existence of moments. Simulation results show significant advantages of the proposed methods in bias reduction, coverage probability, and power. The applications of the proposed methods are illustrated with gene expression profiling of bronchial airway brushing in asthmatic and healthy control subjects. for this article are available online. [ABSTRACT FROM AUTHOR]

Causal inference for extreme events has many potential applications in fields such as climate science, medicine, and economics. We study the extremal quantile treatment effect of a binary treatment on a continuous, heavy-tailed outcome. Existing methods are limited to the case where the quantile of interest is within the range of the observations. For applications in risk assessment, however, the most relevant cases relate to extremal quantiles that go beyond the data range. We introduce an estimator of the extremal quantile treatment effect that relies on asymptotic tail approximation, and use a new causal Hill estimator for the extreme value indices of potential outcome distributions. We establish asymptotic normality of the estimators and propose a consistent variance estimator to achieve valid statistical inference. We illustrate the performance of our method in simulation studies, and apply it to a real dataset to estimate the extremal quantile treatment effect of college education on wage. for this article are available online. [ABSTRACT FROM AUTHOR]

Integrative analysis of data from multiple sources is critical to making generalizable discoveries. Associations consistently observed across multiple source populations are more likely to be generalized to target populations with possible distributional shifts. In this article, we model the heterogeneous multi-source data with multiple high-dimensional regressions and make inferences for the maximin effect (Meinshausen and Bühlmann, AoS, 43(4), 1801–1830). The maximin effect provides a measure of stable associations across multi-source data. A significant maximin effect indicates that a variable has commonly shared effects across multiple source populations, and these shared effects may be generalized to a broader set of target populations. There are challenges associated with inferring maximin effects because its point estimator can have a nonstandard limiting distribution. We devise a novel sampling method to construct valid confidence intervals for maximin effects. The proposed confidence interval attains a parametric length. This sampling procedure and the related theoretical analysis are of independent interest for solving other nonstandard inference problems. Using genetic data on yeast growth in multiple environments, we demonstrate that the genetic variants with significant maximin effects have generalizable effects under new environments. The proposed method is implemented in the R package MaximinInfer available from CRAN. for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract– Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict constraints on the correlation structure among variables. The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data. The theoretical results include both the consistent sparsity pattern recovery as well as statistical inference for each additive functional component. Our approach provides a practical solution for fitting sparse additive models, with promising applications in a wide range of domains. for this article are available online. [ABSTRACT FROM AUTHOR]

In this article, we establish a high-dimensional CLT for the sample mean of p-dimensional spatial data observed over irregularly spaced sampling sites in R d , allowing the dimension p to be much larger than the sample size n. We adopt a stochastic sampling scheme that can generate irregularly spaced sampling sites in a flexible manner and include both pure increasing domain and mixed increasing domain frameworks. To facilitate statistical inference, we develop the spatially dependent wild bootstrap (SDWB) and justify its asymptotic validity in high dimensions by deriving error bounds that hold almost surely conditionally on the stochastic sampling sites. Our dependence conditions on the underlying random field cover a wide class of random fields such as Gaussian random fields and continuous autoregressive moving average random fields. Through numerical simulations and a real data analysis, we demonstrate the usefulness of our bootstrap-based inference in several applications, including joint confidence interval construction for high-dimensional spatial data and change-point detection for spatio-temporal data. for this article are available online. [ABSTRACT FROM AUTHOR]

Sketching algorithms use random projections to generate a smaller sketched data set, often for the purposes of modelling. Complete and partial sketch regression estimates can be constructed using information from only the sketched data set or a combination of the full and sketched data sets. Previous work has obtained the distribution of these estimators under repeated sketching, along with the first two moments for both estimators. Using an alternative approach, we derive the distribution of the complete sketch estimator and additionally consider the error term under both repeated sketching and repeated sampling. Importantly, we obtain pivotal quantities which are based solely on the sketched data set—specifically not requiring information from the full data model fit. These pivotal quantities can be used for inference on the full data set regression estimates or the model parameters. For partial sketching, we derive pivotal quantities for a marginal test and an approximate distribution for the partial sketch under repeated sketching or repeated sampling—again avoiding reliance on a full data model fit. We extend these results to include the Hadamard and Clarkson–Woodruff sketches then compare them in a simulation study. [ABSTRACT FROM AUTHOR]

Purpose: Cough reflex testing (CRT) is an adjunct to the clinical swallowing evaluation (CSE), providing information on patients' risk of silent aspiration. CRT has been shown to influence diet recommendations, but in previous work, the many varied patient characteristics are not controlled. Therefore, the specific role of CRT results in these decisions remains unclear as this relationship has not been directly assessed. Method: An online survey was sent to speech language therapists working in dysphagia. Two patient cases were presented that differed only by the presence of risk factors for the development of aspiration pneumonia. For each patient case, there were three assessment scenarios: CSE information only, CSE information with a "pass" CRT result, and CSE information with a "fail" CRT result. Clinicians outlined their patient management plans for each of the six scenarios. Results: Ninety-seven data sets were used in the final analysis. A "fail" result was found to lead to the most restrictive patient management. Decisions made when provided with only CSE information were very similar to decisions made for a CSE with a "pass" result. Aspiration pneumonia risk factors were shown to influence decision making, with the low-risk patient more likely to be recommended a less restrictive diet. Conclusions: When information was available regarding silent aspiration risk, clinicians factored the results into their decision making. However, in the absence of a CRT result, airway sensation was assumed to be intact in the absence of information. This finding warrants further investigation given the impact this assumption may have on a patient's pulmonary health. [ABSTRACT FROM AUTHOR]

Today, permutation tests represent a powerful and increasingly widespread tool of statistical inference for hypothesis-testing problems. To the best of our knowledge, a review of the application of permutation tests for complex data in practical data analysis for hypothesis testing is missing. In particular, it is essential to review the application of permutation tests in two-sample or multi-sample problems and in regression analysis. The aim of this paper is to consider the main scientific contributions on the subject of permutation methods for hypothesis testing in the mentioned fields. Notes on their use to address the problem of missing data and, in particular, right-censored data, will also be included. This review also tries to highlight the limits and advantages of the works cited with a critical eye and also to provide practical indications to researchers and practitioners who need to identify flexible and distribution-free solutions for the most disparate hypothesis-testing problems. [ABSTRACT FROM AUTHOR]

Background: Supraglottic airways (SGAs) like the laryngeal mask airway (LMA) provide advantages such as faster placement and better haemodynamic stability. LMAs, whether singleuse or reusable, can be crucial in managing difficult airways or maintaining airway patency. This study compares the ProSeal LMA and endotracheal tube (ETT) in patients undergoing laparoscopic surgeries. Methods: A prospective observational study was conducted on 60 participants over one year, with ethical clearance and informed consent. Participants were divided into two groups: 30 received the ProSeal LMA and 30 received the ETT. Data were analyzed using SPSS version 24 with descriptive and inferential statistics, including independent t-tests and Chi-square tests. Results: The ProSeal LMA group had a mean age of 35.73 years, and the ETT group had a mean age of 40.30 years (p > 0.05). ProSeal LMA had faster intubation (13.30 seconds vs. 16.17 seconds) and Ryle's tube insertion (11.30 seconds vs. 13.12 seconds) compared to ETT (p < 0.05). Sore throat incidence was lower in the ProSeal LMA group. Conclusion: The ProSeal LMA offers better stability and similar airway maintenance compared to ETT, suggesting it may be a superior intubation device. [ABSTRACT FROM AUTHOR]

A recent article by Jiang et al. (2022) on generalized linear mixed model asymptotics derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If m denotes the number of groups and n is the average within-group sample size then the asymptotic variances have orders m − 1 and (m n) − 1 ⁠ , depending on the parameter. We extend this theory to provide explicit forms of the (m n) − 1 second terms of the asymptotically harder-to-estimate parameters. Improved accuracy of statistical inference and planning are consequences of our theory. [ABSTRACT FROM AUTHOR]

Limited information is available regarding the knowledge of Cardiovascular Disease (CVD) risk factors and the actual risk behaviors among African American (AA) older women living in rural areas of Alabama. A pilot study of needs assessment for CVD prevention behaviors was conducted to collect such data from rural AA women. This paper reports the quantitative part of the needs assessment of this pilot study. We recruited participants (N = 30) using convenience sampling. Data collection measures included: i) American Heart Association's CVD‐related knowledge questionnaire, ii) Five Times Sit to Stand Test to measure dynamic balance and iii) Six minute walk test (6MWT) to assess exercise endurance iv) standard weighing scale to measure body weight and v) standard measurements scale to measure height. Descriptive and inferential statistics were used to analyze the data. The majority (76%) of participants had hypertension (70%), diabetes (60%), poor balance (70%), and low exercise endurance (100%). Most of the participants had low knowledge related to CVD risk factors. This study demonstrated the need to conduct a large‐scale study to assess knowledge related to heart health and the actual needs and preferences of these individuals. Conducting such a study would lay the foundation for developing a need‐based program for these underserved individuals while incorporating their preferences and the strategies that would help engage them in a heart health intervention. [ABSTRACT FROM AUTHOR]

Introduction: Spaced learning is a teaching method aiming to stabilize long-term memory. The present study aims to evaluate the effect of the spaced learning method on the evaluation scores and education quality in the practical course of principles and techniques of nursing. Materials and Methods: In this quasi-experimental study, 51 nursing students in four semesters were selected by convenience sampling and randomly divided into experimental and control groups. We used the demographic characteristics form, student scores, and students' evaluations of the educational quality questionnaire (SEEQ) for data gathering. In the next step, the classes of the experimental groups were held with the spaced learning method, and the traditional methods were used for the control groups. After the end of each semester, the SEEQ questionnaire was given to the students of both groups, and their evaluation scores were extracted. The collected data were analyzed using SPSS version 26 using descriptive and inferential statistics. Results: After ensuring the independence of the data, the results showed that the difference in the mean evaluation score of the students in the two groups is not statistically significant (p > 0.05). However, the mean score of total SSEQ in the experimental group was significantly higher than the control group. Furthermore, except for the dimension of examination assignments and group interaction, the mean score of other SSEQ dimensions in the experimental group was statistically higher than the control group (p < 0.05). Conclusion: Spaced learning is an easy, low-cost, and fun learning method that affects the teaching method compared to evaluation results and effortlessly has the potential to become an appropriate alternative to traditional teaching methods. [ABSTRACT FROM AUTHOR]

Introduction: Incorporation of AKT inhibitors into adjuvant therapy for advanced or metastatic breast cancer has improved clinical outcomes. However, the safety of AKT inhibitors should be better evaluated, given the possibility of prolonging survival and impacting patient quality of life. Our aim was to assess how the addition of AKT inhibitors to adjuvant therapy affects treatment-related adverse events. Methods: We evaluated binary outcomes with risk ratios (RRs), with 95% confidence intervals (CIs). We used DerSimonian and Laird random-effect models for all endpoints. Heterogeneity was assessed using I2 statistics. R, version 4.2.3, was used for statistical analyses. Results: A total of seven RCTs comprising 1619 patients with BC. The adverse effects that show significance statistical favoring the occurrence of adverse effects in AKT inhibitor were diarrhea (RR 3.05; 95% CI 2.48–3.75; p < 0.00001; I2 = 49%), hyperglycemia (RR 3.4; 95% CI 1.69–6.83; p = 0.00058; I2 = 75%), nausea (RR 1.69; 95% CI 1.34–2.13; p = 0.000008; I2 = 42%), rash (RR 2.79; 95% CI 1.49–5.23; p = 0.0013; I2 = 82%), stomatitis (RR 2.24; 95% CI 1.69–2.97; p < 0.00001; I2 = 16%) and vomiting (RR 2.99; 95% CI 1.85–4.86; p = 0.00009; I2 = 42%). There was no significant difference between the groups for alopecia (p = 0.80), fatigue (p = 0.087), and neuropathy (p = 0.363380). Conclusion: The addition of AKT inhibitors to adjuvant therapy was associated with an increase in treatment-related adverse events. These results provide safety information for further clinical trials evaluating AKT inhibitor therapy for patients with metastatic BC. Clinicians should closely monitor patients for treatment-related adverse events to avoid discontinuation of therapy and morbidity caused by these early-stage therapies. [ABSTRACT FROM AUTHOR]

In diagnostic studies, a continuous biomarker is often dichotomized for the diagnosis of binary disease status. Various criteria have been studied for the cut point selection of the continuous biomarker in receiver operating characteristic (ROC) analysis, in particular, the Youden index, the closest-to-(0,1) index, and the concordance probability index. Recently, Wang, Tian, and Zhao (2017) established a Wilks theorem for a smoothed empirical likelihood ratio statistic of Youden index. However, it is not directly useful for statistical inference compared to the cut point. In addition, the optimal cut point may vary with different criteria. In this article, we study smoothed empirical likelihood for optimal cut point selection with Youden index, closest-to-(0,1) criterion, and concordance probability. We develop confidence estimation for the optimal cut points based on the smoothed empirical likelihood ratio statistics. We examine the empirical performance by extensive simulation studies. We also illustrate the method with a real dataset. [ABSTRACT FROM AUTHOR]

Surface sampling and laboratory analysis for soot/combustion particulates was conducted following a fire at an education/research facility in the southwest United States. This provided a bank of data by which to probabilistically evaluate the behavior of soot loading (counts/mm2) and relative soot concentration (percent ratio; %R) as useful metrics for quantifying differences in soot impact across a building. Surface tape sampling and analysis via light microscopy were conducted via industry standard protocols, and resulting data from various building zones were selected to construct various comparisons. The performance of counts/mm2 and %R as metrics to identify differences in soot impact for each comparison was assessed by comparing inference generated by traditional Student's t test, Mann Whitney U rank comparison (MW), and the directly calculated axiomatic probability associated with difference in detection (pΔfd). The fourteen (14) comparisons in which a significant difference was inferred via pΔfd was similarly indicated via Student's t and/or MW in only four (4) instances. Further, approximately one half of the comparisons generated different inference via pΔfd for counts/mm2 and %R, with the former demonstrating better discriminatory ability. In broad view, the heuristic concept of comparing numerical "soot levels" (e.g., average) by either metric was not generally suitable for the distribution of the data. In contrast, pΔfd avoids the statistical bias imposed by traditional statistical inference, and ultimately the efficacy of post fire comparative surface sampling is as dependent upon the metric and inference model utilized as it is on the sampling and laboratory analytical protocols. [ABSTRACT FROM AUTHOR]

Background: Robot-assisted procedures are increasingly common, and several systems are available for thoraco-abdominal surgery. Specific structured training is necessary, while access to these systems is still limited. This study aimed to assess surgeons' skill progress during consecutive training days of a curriculum with exposure to different robotic systems. Methods: This prospective observational study enrolled 47 surgeons with anonymized analysis of SimNow™ simulator performance scores and dedicated questionnaires after written consent. The primary outcome was the overall score, based on economy of motion, time to complete the exercise, and penalty for errors. Course participants in 2022–2023 had chosen 2 full hands-on days on Da Vinci® consoles with either virtual reality (VR) simulation training using the SimNow (n = 21, 44.7%) or digestive surgery procedures with a live animal model (n = 26, 55.3%). In all participants, training on Da Vinci® systems included console functions and principles of docking, camera, and instrument use for console and procedural training. They additionally had access to introductory dry-lab and VR simulator exercises on the Versius, HugoTMRAS, and Dexter systems and to VR exercises on the ROBOTiS simulator. Results: The participants (16F/31M, median age 40 years, range 29–58) from various surgical specialties (general/visceral/vascular) had no (n = 35, 74.5%) or little (n = 12, 25.5%) robotic experience including bedside assistance only and 20 (42.6%) had robotic simulator experience. The demographic variables fully completed by 44/47 participants (93.6%) and choice of module had no significant impact on the primary outcome. The considerable performance improvement from days 1 to 2 was exemplified by a significantly increased economy of motion and decreased amount of excessive force. Conclusion: Robotic surgical training is increasingly complex with several systems on the market. Within a dedicated robotic surgery curriculum and based on integrated performance metrics, a significant improvement of skill levels was observed in a relatively short period of time. [ABSTRACT FROM AUTHOR]

Background: The repair of inguinal hernias is a common surgical procedure. Some patients will need a second operation after developing a metachronous contralateral inguinal hernia (MCIH). The suggestion has been made to strengthen the contralateral side at the same time as primary unilateral surgery. In this systematic review we aim to determine how many adult patients with a unilateral inguinal hernia will develop a MCIH. Methods: This review was reported in line with PRISMA guidelines. We performed a literature review in PubMed Publisher, Embase, Google Scholar and Cochrane Library until May 2021, including articles reporting MCIH after unilateral inguinal hernia repair in adults. As outcome measure, the number of patients diagnosed with a MCIH was registered. Results: A total of 19 articles with a total of 277,288 patients were included. The combined estimate for MCIH after meta-analysis equaled 8.3% (95% CI 7.1%–9.5%), with a high index of heterogeneity (I2 = 97.9%). A random-effects meta-regression was performed to obtain the percentage of MCIH as a function of median follow-up time. The percentage of MCIH at 3, 5 and 10 years was estimated at 5.2%, 8.0% and 17.1%, respectively. Conclusion: We suggest that there is insufficient clinical evidence to support prophylactic contralateral repair in all patients. We propose a patient-specific approach in the decision to perform prophylactic repair, taking into account potential risk factors for hernia development, type of surgical approach, and general risk factors for chronic postoperative inguinal pain. More long-term prospective data are needed to guide the decision for prophylactic contralateral mesh placement. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To determine the effects of music intervention on quality of life, anxiety, and fatigue among patients with breast cancer. SAMPLE & SETTING: 170 individuals from the general surgery unit of Kaohsiung Chang Gung Memorial Hospital in Taiwan. METHODS & VARIABLES: Individuals who had received a mastectomy were randomly assigned to a treatment group or control group. In the treatment group, participants received music intervention for an hour each week, totaling 12 hours during 12 consecutive weeks. The primary variable was quality of life, and secondary variables were anxiety and fatigue. RESULTS: Greater quality of life was seen in the treatment group at 12 weeks. In the treatment group, participants showed significant improvement in State-Trait Anxiety Inventory (trait), which was most significant at eight weeks. No statistically significant difference was observed in fatigue levels after 4, 8, and 12 weeks of music intervention. IMPLICATIONS FOR NURSING: Music intervention may improve anxiety and quality of life of patients with breast cancer. Music intervention could be established following mastectomy and continued throughout recovery. [ABSTRACT FROM AUTHOR]

While the lack of educational opportunities limits the future of most people in Ethiopia, they pose dire consequences to young people in migration hotspot areas. Using input‐process‐outcome‐context‐education quality framework, this study investigated the quality of education in eight migration hotspot areas of Ethiopia and its association with migration. A random sample of 1,187 participants (793 students, 262 teachers and 132 parents) completed a survey. Data were analysed using descriptive and inferential statistics. Results indicated that participants' overall rating of education quality was low. Over a third of participants perceived that input, process and outcome components of education in migration hotspot areas have a quality problem. Over 80% of the participants believed that poor quality of education could be a cause for migration. More specifically, a significant portion of the participants (37%) reported that there was no pedagogical centre in their respective schools; continuous assessment was practiced rarely (36%); and creativity and discovery among students was almost non‐existent (40%). The participants' ratings indicated that the top five factors that contribute to the poor quality of education are low level of teacher competence, poor student motivation, poverty, value attached to education and large class size. [ABSTRACT FROM AUTHOR]

Teachers are uniquely positioned to mitigate ableism and address disability in the classroom; however, many hesitate to do so. This silence perpetuates bias and may teach children that difference is negative. Situated within the field of Disability Studies in Education, this investigation of PK-12 teachers (n = 256) aims to broadly characterise the types of discussions that occur in classrooms as well as identify obstacles and facilitators associated with disability discussions. Data reveal 71.9% of participants currently discuss disability with their students, mostly in relation to students' own learning needs. Findings indicate that internal, rather than external, factors drive teachers' decisions to discuss disability, laying the groundwork for pre-service and in-service programming to increase disability-related conversation. [ABSTRACT FROM AUTHOR]

While the Taguchi capability index developed by Chan (J Qual Technol 20(3):162–175, 1988) takes the process targeting issue into consideration, it fails to account for processes with asymmetric tolerances, which are common in practice. Thus, Chen (Int J Reliab Qual Saf Eng 6(4):383–398) modified this index to include processes with asymmetric tolerances. This index is an important tool for the assessment of quality characteristics with asymmetric tolerances, which are common in practice. As the probability density function of the index is complex, statistical inference can be fairly difficult for quality or process engineers. Furthermore, sample sizes are often small in practice to increase decision-making efficiency, but this can decrease assessment accuracy. To address this issue, we employed a mathematical programming approach to make it more convenient for quality or process engineers to derive the upper confidence limit of the index. We also adopted the suggestion put forward by previous studies to incorporate historical data or expert experience in confidence-interval-based fuzzy testing. The proposed approach therefore has increased assessment accuracy, is convenient to apply in practice, and meets the need for swift responses. [ABSTRACT FROM AUTHOR]

This work is motivated by a gap in the functional data analysis literature, particularly in the context of neuroimaging, regarding the ability of functional models to robustly accommodate intra‐observation dependence. In response, we propose an M‐estimator based on generalized empirical likelihood for the varying‐coefficient model with a functional response. We develop statistical inference procedures, simultaneous confidence regions, and a global general linear hypothesis test for the model's functional coefficient. Our theoretical results establish the weak convergence of the log‐likelihood ratio process, a nonparametric version of Wilks' theorem for the log‐likelihood ratio, and asymptotic properties of the proposed estimator. Through a simulation study, we show that the proposed confidence sets have close‐to‐nominal coverage probabilities. In a real‐world application to a neuroimaging dataset, we show that mini‐mental state examination score and apolipoprotein E genotype have significant associations with fractional anisotropy, while associations with gender and age are only present at high quantile levels. [ABSTRACT FROM AUTHOR]

This paper studies generative adversarial networks (GANs) from the perspective of statistical inference. A GAN is a popular machine learning method in which the parameters of two neural networks, a generator and a discriminator, are estimated to solve a particular minimax problem. This minimax problem typically has a multitude of solutions and the focus of this paper are the statistical properties of these solutions. We address two key statistical issues for the generator and discriminator network parameters, consistent estimation and confidence sets. We first show that the set of solutions to the sample GAN problem is a (Hausdorff) consistent estimator of the set of solutions to the corresponding population GAN problem. We then devise a computationally intensive procedure to form confidence sets and show that these sets contain the population GAN solutions with the desired coverage probability. Small numerical experiments and a Monte Carlo study illustrate our results and verify our theoretical findings. We also show that our results apply in general minimax problems that may be nonconvex, nonconcave, and have multiple solutions. [ABSTRACT FROM AUTHOR]

Linear Mixed Effects (LME) models are powerful statistical tools that have been employed in many different real-world applications such as retail data analytics, marketing measurement, and medical research. Statistical inference is often conducted via maximum likelihood estimation with Normality assumptions on the random effects. Nevertheless, for many applications in the retail industry, it is often necessary to consider non-Normal distributions on the random effects when considering the unknown parameters' business interpretations. Motivated by this need, a linear mixed effects model with possibly non-Normal distribution is studied in this research. We propose a general estimating framework based on a saddlepoint approximation (SA) of the probability density function of the dependent variable, which leads to constrained nonlinear optimization problems. The classical LME model with Normality assumption can then be viewed as a special case under the proposed general SA framework. Compared with the existing approach, the proposed method enhances the real-world interpretability of the estimates with satisfactory model fits. [ABSTRACT FROM AUTHOR]

In maximum likelihood methods, the three classical tests statistics are often unreliable for inference in small samples, even under the correct model specification. In this article, I discuss how likelihood ratio and Wald test statistics can be combined to obtain highly improved parameter inference in regression models with small samples. I consider modifications obtained from both the Barndorff-Nielsen and the Lugannani and Rice likelihood approximations, and I show how they can produce highly accurate parameter inference in a general (possibly non linear) regression model with possibly non spherical disturbances. I discuss the underlying theory and provide Monte Carlo simulations demonstrating the superior accuracy of the proposed procedures over the first-order classical likelihood methods (i.e., the signed log-likelihood ratio test and the Wald test). An empirical application to a regression model of mobile money ("M-pesa") adoption in Kenya is provided as an illustration of the usefulness of these methods in practice. [ABSTRACT FROM AUTHOR]

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated. [ABSTRACT FROM AUTHOR]

Parkinson's disease is related to nervous system disorders in human beings. In this paper, the status of Parkinson's disease has been predicted based on the analysis of features of two Parkinson's disease symptoms: Dysphonia and Dysarthria. The Dysphonia symptom has been measured by analyzing the features extracted from the vocal sounds of a patient. The Dysarthria symptom of Parkinson's disease has been analyzed by the features extracted from the speech signals of a patient. Hence, two different prediction models have been built for deriving the status of Parkinson's disease. The derivation of these prediction systems is based on statistical and discriminant feature analysis. The statistical-based feature analysis is carried out using descriptive and inferential analysis. In contrast, the enteric, deviation, and correlation-based information between the features are utilized for descriptive analysis. The inferential analysis has been performed using hypothetical testing of the distribution of features. The descriptive and inferential feature analyses are used to derive effective features for the Parkinson's disease problem based on the Dysphonia and Dysarthria symptoms. In the discriminant feature analysis, the feature sets undergo deriving discriminant features. The best-selected features are used to build a prediction model for detecting Parkinson's disease problems in a patient. Extensive experimentation has been carried out using a Dysphonia dataset containing 195 samples. Each sample has 23 features. One dataset for Dysarthria symptoms contains 756 observations, with each observation having 754 features. The best prediction models that have been obtained correspond to these datasets, and 93.50% is obtained for Dysphonia and 87.63% for Dysarthria symptoms databases, respectively. The performance of the proposed system has been compared with some existing methods concerning each employed dataset, showing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Objectives: Clinical associate psychologists (CAPs) train under the auspices of the apprenticeship programme and are a new addition to the psychological workforce. This project sought to evaluate whether a "personal reformulation" (PR) was helpful in terms of personal and professional development during the apprenticeship. Methods: A mixed methods evaluation containing a longitudinal quantitative element and a "Big Q" qualitative element with a single cohort of N = 18 CAPs. A PR consists of a 2‐hr one‐to‐one session and a follow‐up session with a cognitive analytic psychotherapist. During a PR, a sequential diagrammatic reformulation is produced to aid recognition and revision of potentially problematic relationship patterns at work. Two outcome measures concerning reflective capacity and professional quality of life were completed at the start of the apprenticeship, pre‐PR and at 3‐month PR follow‐up. The semi‐structured interviews (n = 11) conducted at the follow‐up were analysed using reflexive thematic analysis. Results: Quantitative changes were limited to significant increases to general confidence and 8/12 apprentices had a reliable increase in confidence in communication. Qualitatively, five overarching themes were found: (1) gaining insight, (2) wellbeing, (3) nature of the space, (4) being an apprentice, and (5) moving forward. Conclusions: PRs were generally found to be emotionally challenging, but relatively large amounts of insight are possible from a very brief intervention that can contribute to personal and professional development during clinical training. More controlled research needs to be conducted and wider applications and evaluations of PRs in different professions would be welcome. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is a complex problem characterized by frequent hospitalizations and high 30-day readmission rates. Researchers studying HF readmission report that patients and clinicians have different perspectives on readmission and preventability when unadjusted for disease severity. Objective: The aim of this study was to gather patient, caregiver, nurse, and physician subjective reason(s) for 30-day HF readmission and perceptions of preventability with contextual factors to evaluate differences. Methods: A convergent, parallel, mixed-methods design was used with interviews and chart reviews to evaluate contextual factors from the current and index hospital stay. Adults readmitted within 30 days of a previous inpatient stay with a coded HF diagnosis were enrolled and interviewed, followed by interviews with associated caregivers, attending physicians, and assigned nurses. Results: Interviews were conducted with patients (n = 44), caregivers (n = 6), physicians (n = 24), and nurses (n = 44). Readmissions were emergent/urgent (95%) and occurred within 14.9 days (SD, 8.1; 2-28 days) on average after discharge. Index stay coding revealed that most patients (73%) had a high severity of illness (73%) and risk of mortality (68%). Heart failure stage was inconsistently documented. Patients reported acute symptomatic reasons, with only 32% describing readmission as preventable. Physicians reported diagnostic reasons, 38% of which were preventable. Nurses reported behavioral reasons, with 59% being preventable. Patient/clinician agreement on readmission reason was low(30%). Conclusions: Patient/clinician perspectives on readmission varied among the patients with complex HF. Care planning based on HF stage and other contextual factors is needed to ensure a shared understanding of disease severity and a tailored symptom management approach to prevent readmission. [ABSTRACT FROM AUTHOR]

determinants of access to rehabilitation professionals after stroke in middle-income countries, where the burden of this disease is higher, are little known. To identify the determinants of access to rehabilitation professionals by individuals with stroke at one, three, and six months after hospital discharge in Brazil and compare referral and access rates after discharge. Longitudinal and prospective study, with individuals with primary stroke, without previous disabilities. At hospital discharge, the number of rehabilitation professionals referred by the multidisciplinary team was recorded. The possible determinants of access, according to Andersen's model, were: a) predisposing factors: age, sex, education levels, and belief that they could improve with treatment; b) need factors: stroke severity, levels of disability; c) enabling factors: socioeconomic status, disposable income for health care, and quality of care provided by rehabilitation professionals. One, three, and six months after hospital discharge, individuals were contacted to identify which rehabilitation professionals were accessed. Multiple linear regression model and Wilcoxon tests were used (α=5%). 201 individuals were included. Disability levels and stroke severity explained 31%, 34%, and 39% (p<0.01) of access at one, three, and six months after hospital discharge, respectively. In all periods, there was less access than that recommended at the time of hospital discharge (p<0.01). Need factors (disability levels and stroke severity) were determinants of access in all assessed periods. In addition, in all periods, the comprehensiveness of care for individuals with stroke was compromised. [ABSTRACT FROM AUTHOR]

Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL. Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC. [ABSTRACT FROM AUTHOR]

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non- overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed at investigating an already-proposed problem-based model of writing on Iranian EFL learners' writing skill. Methods: In so doing, an experimental pre-test post-test control group study was designed. The participants of the study consisted of 60 female advanced EFL learners who were randomly selected from eighty-eight learners who were passing an IELTS preparation course at a private language institute. The study sample was randomly assigned into two groups (each consisting of 30 learners) namely, the experimental and the control groups. Two samples of IELTS Writing Task 2 were used for the purpose of measuring the participants' writing skill at the beginning (pre-test) and end (post-test) of the study. To analyze the data, normality test, descriptive and inferential statistics (i.e., analysis of covariance (ANCOVA) test) was used. Results: The results of data analysis showed that the proposed problem-based model of writing affects Iranian EFL learners' writing skill. Conclusions: The findings have pedagogical implications for EFL teachers and learners, and curriculum planners. [ABSTRACT FROM AUTHOR]