Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.     

Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

Combinatorial data analysis: Association and partial association

A combinatorial data analysis strategy is reviewed that is designed to compare two arbitrary measures of proximity defined between the objects from some set. Based on a particular cross-product definition of correspondence between these two numerically specified notions of proximity (typically represented in the form of matrices), extensions are then pursued to indices of partial association that relate the observed pattern of correspondence between the first two proximity measures to a third. The attendant issues of index normalization and significance testing are discussed; the latter is approached through a simple randomization model implemented either through a Monte Carlo procedure or distributional approximations based on the first three moments. Applications of the original comparison strategy and its extensions to partial association may be developed for a variety of methodological and substantive tasks. Besides rank correlation, we emphasize the topics of spatial autocorrelation for one variable and spatial association between two and mention the connection to the usual randomization approach for one-way analysis-of-variance.     

The effect of COVID-19 on home advantage in high- and low-stake situations: Evidence from the European national football competitions

The Covid-19 pandemic has significantly altered the way sporting events are observed. With the absence or limited presence of spectators in stadiums, the traditional advantage enjoyed by home teams has diminished considerably. This underscores the notion that the support of home fans can often be considered a key factor of the home advantage (HA) phenomenon, wherein teams perform better in front of their own supporters. However, the impact of reduced attendance on games with higher stakes, as opposed to low-stakes friendly matches, remains uncertain. In this study, we investigate the recently concluded European football championship (EURO 20), wherein several teams had the advantage of playing at home in high-stakes games with only one-third of the stadium capacity filled. Firstly, we demonstrate that the Covid-19 restrictions, leading to reduced fan attendance, resulted in a nearly 50% decrease in HA compared to the HA exhibited by the same teams during the qualification stage preceding EURO 20, even after accounting for team strength. Secondly, we show that while low-stakes friendly matches generally exhibit a smaller overall HA compared to high-stakes games, the absence of fans led to a similar reduction in HA during the low-stakes matches. Utilizing the recently developed Home Advantage Mediated (HAM) model (Bilalić, Gula, & Vaci, 2021, Scientific Reports, 21558), we were able to attribute the reduction in both high- and low-stakes games to poorer team performance, with no significant contribution from referee bias.     

Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.     

Time-series analysis in operant research

A time-series method is presented, nontechnically, for analysis of data generated in individual-subject operant studies, and is recommended as a supplement to visual analysis of behavior change in reversal or multiple-baseline experiments. The method can be used to identify three kinds of statistically significant behavior change: (a) changes in score levels from one experimental phase to another, (b) reliable upward or downward trends in scores, and (c) changes in trends between phases. The detection of, and reliance on, serial dependency (autocorrelation among temporally adjacent scores) in individual-subject behavioral scores is emphasized. Examples of published data from the operant literature are used to illustrate the time-series method.     

Considerations in the choice of interobserver reliability estimates

Two types of interobserver reliability values may be needed in treatment studies in which observers constitute the primary data-acquisition system: trial reliability and the reliability of the composite unit or score which is subsequently analyzed, e.g., daily or weekly session totals. Two approaches to determining interobserver reliability are described: percentage agreement and "correlational" measures of reliability. The interpretation of these estimates, factors affecting their magnitude, and the advantages and limitations of each approach are presented.     

The Effect of Evidential Impact on Perceptual Probabilistic Judgments

In a series of three behavioral experiments, we found a systematic distortion of probability judgments concerning elementary visual stimuli. Participants were briefly shown a set of figures that had two features (e.g., a geometric shape and a color) with two possible values each (e.g., triangle or circle and black or white). A figure was then drawn, and participants were informed about the value of one of its features (e.g., that the figure was a “circle”) and had to predict the value of the other feature (e.g., whether the figure was “black” or “white”). We repeated this procedure for various sets of figures and, by varying the statistical association between features in the sets, we manipulated the probability of a feature given the evidence of another (e.g., the posterior probability of hypothesis “black” given the evidence “circle”) as well as the support provided by a feature to another (e.g., the impact, or confirmation, of evidence “circle” on the hypothesis “black”). Results indicated that participants’ judgments were deeply affected by impact, although they only should have depended on the probability distributions over the features, and that the dissociation between evidential impact and posterior probability increased the number of errors. The implications of these findings for lower and higher level cognitive models are discussed.     

贝叶斯统计方法的应用与现状

贝叶斯统计方法是心理学数据分析的热门方法。研究全面论述贝叶斯方法在心理学领域的应用与方向。现阶段贝叶斯方法以模拟研究为主,应用方向为心理学研究常用的项目反应理论、认知诊断、计算机自适应、结构方程模型。同时,评述发现贝叶斯方法正逐步被国内心理学研究者所接受。最后,文章讨论了当下贝叶斯统计在心理学研究中应用的局限性及可能的原因,建议统计学者开发界面友好的贝叶斯软件,并在心理学课程中加入贝叶斯知识。