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There is a limited body of experimental research examining the comparability of completing self-report surveys using different computerized devices. Additionally, available literature has not used complete or optimal procedures for determining device equivalence. The current study examined the comparability of surveys completed using paper-and-pencil and three popular devices: smartphone, tablet, and desktop computer. Participants consisted of 211 college students randomly assigned to conditions who completed measures of personality, social desirability, and computer self-efficacy. Results showed evidence of qualitative equivalence (internal consistency and subscale intercorrelations) across conditions. For quantitative and auxiliary equivalence, both equivalence testing and Bayesian analyses were conducted. Equivalence testing indicated quantitative (mean score) equivalence, as well as comparability for one aspect of auxiliary equivalence (missing data). Other aspects of auxiliary equivalence (completion time and comfort completing questionnaires) suggested potentially meaningful differences. Bayesian analyses typically replicated these results, with some notable exceptions regarding auxiliary equivalence. [ABSTRACT FROM AUTHOR]

P values linked to null hypothesis significance testing (NHST) is the most widely (mis)used method of statistical inference. Empirical data suggest that across the biomedical literature (1990–2015), when abstracts use P values 96% of them have P values of 0.05 or less. The same percentage (96%) applies for full-text articles. Among 100 articles in PubMed, 55 report P values, while only 4 present confidence intervals for all the reported effect sizes, none use Bayesian methods and none use false-discovery rate. Over 25 years (1990–2015), use of P values in abstracts has doubled for all PubMed, and tripled for meta-analyses, while for some types of designs such as randomized trials the majority of abstracts report P values. There is major selective reporting for P values. Abstracts tend to highlight most favorable P values and inferences use even further spin to reach exaggerated, unreliable conclusions. The availability of large-scale data on P values from many papers has allowed the development and applications of methods that try to detect and model selection biases, for example, p-hacking, that cause patterns of excess significance. Inferences need to be cautious as they depend on the assumptions made by these models and can be affected by the presence of other biases (e.g., confounding in observational studies). While much of the unreliability of past and present research is driven by small, underpowered studies, NHST with P values may be also particularly problematic in the era of overpowered big data. NHST and P values are optimal only in a minority of current research. Using a more stringent threshold, as in the recently proposed shift from P < 0.05 to P < 0.005, is a temporizing measure to contain the flood and death-by-significance. NHST and P values may be replaced in many fields by other, more fit-for-purpose, inferential methods. However, curtailing selection biases requires additional measures, beyond changes in inferential methods, and in particular reproducible research practices. [ABSTRACT FROM AUTHOR]

P values linked to null hypothesis significance testing (NHST) is the most widely (mis)used method of statistical inference. Empirical data suggest that across the biomedical literature (1990-2015), when abstracts use P values 96% of them have P values of 0.05 or less. The same percentage (96%) applies for full-text articles. Among 100 articles in PubMed, 55 report P values, while only 4 present confidence intervals for all the reported effect sizes, none use Bayesian methods and none use false-discovery rate. Over 25 years (1990-2015), use of P values in abstracts has doubled for all PubMed, and tripled for meta-analyses, while for some types of designs such as randomized trials the majority of abstracts report P values. There is major selective reporting for P values. Abstracts tend to highlight most favorable P values and inferences use even further spin to reach exaggerated, unreliable conclusions. The availability of large-scale data on P values from many papers has allowed the development and applications of methods that try to detect and model selection biases, for example, p-hacking, that cause patterns of excess significance. Inferences need to be cautious as they depend on the assumptions made by these models and can be affected by the presence of other biases (e.g., confounding in observational studies). While much of the unreliability of past and present research is driven by small, underpowered studies, NHST with P values may be also particularly problematic in the era of overpowered big data. NHST and P values are optimal only in a minority of current research. Using a more stringent threshold, as in the recently proposed shift from P < 0.05 to P < 0.005, is a temporizing measure to contain the flood and death-by-significance. NHST and P values may be replaced in many fields by other, more fit-for-purpose, inferential methods. However, curtailing selection biases requires additional measures, beyond changes in inferential methods, and in particular reproducible research practices. [ABSTRACT FROM AUTHOR]

This paper presents a method for developing and testing a risk-based control system, as a first step towards including the human supervisor explicitly in the design of the system. The result is a control system with improved decision-making capabilities compared to existing control systems. The methodology presented in the paper uses the Systems Theoretic Process Analysis (STPA) to analyse the risks of an autonomous ship within its concept of operations (CONOPS), and a Human-STPA (H-STPA) is used to analyse human responsibilities and involvement. The STPA results are then used to construct a Bayesian belief network (BBN)-based risk model to assess the operational risk of the ship. This is represented as a risk cost, describing the expected cost of consequences caused by potential hazardous events. This cost is combined with fuel costs, operations costs, and the potential loss of income if new missions are not undertaken using a supervisory risk controller (SRC). The SRC is capable of making decisions about how the ship should be safely operated and notifies the human supervisor in due time when it is necessary for them to take control. The last part of the methodology presented in this paper is testing the control system using a set of verification objectives based on results from the STPA and H-STPA. A case study involving an autonomous cargo ship with a human supervisor located in a remote operation center (ROC) is included; it shows that the proposed control system can operate the ship safely in different conditions and situations. By designing the SRC to notify the human supervisor before it reaches its operational limit, the ship is able to operate in a wider range of conditions compared to when just the autonomous control system is in charge. Hence, the proposed methodology shows promising results and provides useful insights related to shared control for autonomous ships. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for analyzing bivariate positive data, taking into account a covariate vector and left-censored observations, by introducing a hierarchical Bayesian analysis. The proposed method assumes marginal Weibull distributions and employs either a usual Weibull likelihood or Weibull–Tobit likelihood approaches. A latent variable or frailty is included in the model to capture the possible correlation between the bivariate responses for the same sampling unit. The posterior summaries of interest are obtained through Markov Chain Monte Carlo methods. To demonstrate the effectiveness of the proposed methodology, we apply it to a bivariate data set from stellar astronomy that includes left-censored observations and covariates. Our results indicate that the new bivariate model approach, which incorporates the latent factor to capture the potential dependence between the two responses of interest, produces accurate inference results. We also compare the two models using the different likelihood approaches (Weibull or Weibull–Tobit likelihoods) in the application. Overall, our findings suggest that the proposed hierarchical Bayesian analysis is a promising approach for analyzing bivariate positive data with left-censored observations and covariate information. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we propose a flexible cure rate survival model by assuming the number of competing causes, related to the event of interest, to follow generalized Poisson distribution. The generalized Poisson distribution has several important properties, e.g. it can effectively model both underdispersed (mean > variance) and overdispersed (mean < variance) mechanisms present in the data. Further, it includes the promotion time cure model as a special case. In this work, for a non-cure subject, we model the time to event of interest with the piecewise exponential distribution. We emphasize that the piecewise exponential distribution has a greater flexibility to model failure times, and can be considered as a distribution-free alternative to the parametric distributions based assumptions. Inferences for the model parameters are considered via full Bayesian analysis. Further, a detailed simulation study is performed with different parameter settings, sample sizes and censoring percentages to study the behavior of the estimators. Finally, a data set from the medical area is analyzed for further illustration. [ABSTRACT FROM AUTHOR]

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management. [ABSTRACT FROM AUTHOR]

In phase 2 clinical trials, we expect to make a right Go or No-Go decision during the interim analysis (IA) and make this decision at the right time. The optimal time for IA is usually determined based on a utility function. In most previous research, utility functions aim to minimize the expected sample size or total cost in confirmatory trials. However, the selected time can vary depending on different alternative hypotheses. This paper proposes a new utility function for Bayesian phase 2 exploratory clinical trials. It evaluates the predictability and robustness of the Go and No-Go decision made during the IA. We can make a robust time selection for the IA based on the function regardless of the treatment effect assumptions. [ABSTRACT FROM AUTHOR]

This is the fourth contribution within an ongoing series dedicated to the phylogeny and taxonomy of powdery mildews. This particular installment undertakes a comprehensive evaluation of a group previously referred to as the "Uncinula lineage" within Erysiphe. The genus Erysiphe is too large to be assessed in a single paper; thus, the treatment of Erysiphe is split into three parts, according to phylogenetic lineages. The first paper, presented here, discusses the most basal lineage of Erysiphe and its relationship to allied basal genera within tribe Erysipheae (i.e., Brasiliomyces and Salmonomyces). ITS+28S analyses are insufficient to resolve the basal assemblage of taxa within the Erysipheae. Therefore, phylogenetic multilocus examinations have been carried out to better understand the evolution of these taxa. The results of our analyses favor maintaining Brasiliomyces, Bulbomicroidium, and Salmonomyces as separate genera, at least for the interim, until further phylogenetic multilocus data are available for additional basal taxa within the Erysipheae. The current analyses also confirmed previous results that showed that the "Uncinula lineage" is not exclusively composed of Erysiphe species of sect. Uncinula but also includes some species that morphologically align with sect. Erysiphe, as well as species that had previously been assigned to Californiomyces and Typhulochaeta. Numerous sequences of Erysiphe species from the "Uncinula lineage" have been included in the present phylogenetic analyses and were confirmed by their position in well-supported species clades. Several species have been sequenced for the first time, including Erysiphe clintonii, E. couchii, E. geniculata, E. macrospora, and E. parvula. Ex-type sequences are provided for 16 taxa including E. nothofagi, E. trinae, and E. variabilis. Epitypes are designated and ex-epitype sequences are added for 18 taxa including Erysiphe carpophila, E. densa, and U. geniculata var. carpinicola. The new species Erysiphe canariensis is described, and the new names E. hosagoudarii and E. pseudoprunastri and the new combination E. ampelopsidis are introduced. [ABSTRACT FROM AUTHOR]

Human reliability analysis (HRA) is approaching nearly 60 years of reliance on key aspects of the original HRA method Technique for Human Error Rate Prediction (THERP), including its process for analyzing dependency. Despite advances in computational abilities and HRA-relevant techniques, the conceptualization, modeling, and quantification of dependency have remained largely unchanged since the introduction of THERP. As a result, current HRA methods do not consider dependency in a realistic manner, and there remain foundational gaps related to the definition, lack of causality, and quantification for HRA dependency. In this paper, we review the current conceptualization of dependency and demonstrate that current research in dependency is not addressing all of the technical gaps. To address the outstanding technical gaps in HRA dependency, we propose a set of fundamental dependency structures (HRA dependency idioms) that capture the spectrum of relationships possible between HRA variables. The idioms provide a robust logical structure for HRA dependency that emphasizes causality and is based on a causal Bayesian network modeling architecture. The idioms conceptualize and model HRA dependency in an objective, traceable, and causally informed manner that facilitates data-based quantification of HRA dependency. [ABSTRACT FROM AUTHOR]

AbstractThe effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects. [ABSTRACT FROM AUTHOR]

The Lomax distribution is one of the well-known distributions that is used to fit heavy-tailed data. In this paper, we investigate the estimation of Shannon entropy of the Lomax distribution using noninformative priors. Some important priors including Jeffreys prior, reference priors and probability matching priors are presented. We demonstrate that the reference and matching priors for the Lomax entropy do not match the reference and probability matching priors of the Lomax parameters, regardless of which parameter of interest is considered. The propriety and the existence of the expectation of the posterior under each prior are validated, respectively. A simulation study is conducted to assess the frequentist performance of the proposed Bayesian estimates in terms of the mean squared error and coverage probability. Finally, the approach is applied to three real data sets for illustrative purposes. [ABSTRACT FROM AUTHOR]

For a model incorporating the effect of a common environment on several components of a system, a multivariate Lomax distribution (MLD) is generally considered by mixing exponential variables. Objective Bayesian has very good frequentist properties and provides a moderate solution for the prior elicitation which is one of important and difficult issues on Bayesian analysis. In this paper, we develop noninformative priors, such as the probability matching priors and reference priors, for the parameters of the MLD. We proved that a reference prior for the shape parameter is a first-order probability matching prior, but the reference priors for the scale parameters do not satisfy the first-order matching criterion. In addition, a second-order probability matching prior does not exist for all parameters. We also presented the conditions that make the posterior distributions for the general prior, including the probability matching prior and reference priors, to be proper. In particular, Jeffreys' prior and probability matching priors for all parameters give proper posteriors, whereas reference priors for scale parameters give improper posteriors. [ABSTRACT FROM AUTHOR]

Life expectancy and healthy life expectancy are both important indicators of population wellbeing, reflecting the expected length of life and duraiton of healthy living respectively. While extensive research has focused on projecting life expectancy, very few have investigated the projection and simulation of healthy life expectancy and examined its potential applications. In this paper, we establish a Bayesian approach to jointly model life expectancy and healthy life expectancy. We apply this approach to the population data of various countries, demonstrating its ability to provide accurate forecasts, generate probability intervals to capture future uncertainty, and estimate missing data values. We then illustrate an application of the Bayesian model in valuing a retirement village contract. Many Australian seniors choose retirement villages for the community support they offer, transitioning to aged care facilities as their health declines. The value of the accommodation service depends on the resident's length of stay, which can be approximated by healthy life expectancy. The proposed Bayesian model can produce a sampled distribution of the contract's net present value, providing a useful risk management tool for retirement village operators. Our findings emphasise the importance of considering healthy life expectancy in policies on public health and retirement. [ABSTRACT FROM AUTHOR]

In most western legal systems, only the fact-finder (judge or jury) is entrusted to make the ultimate decision in a criminal case. A forensic expert can help the fact-finder by opining on the weight of the forensic evidence given the hypotheses relevant to the case, but is not qualified to give an opinion about the ultimate question(s). When the question is reduced to two simple hypotheses, a Bayes Factor can express the expert’s opinion about the extent to which the forensic evidence favours each hypothesis. This paper addresses the situation in which one or both of the hypotheses are composite, that is, embrace more than one possibility. It offers an interval of Bayes Factors, and shows that the proposed interval includes those values, and only those values, of the Bayes Factor supported by possible beliefs of the fact-finder. Shoe prints, tool marks and DNA are discussed in this light if the hypotheses used in the Bayes Factor are composite. [ABSTRACT FROM AUTHOR]

In this paper, the Bayesian inference of the model of dependent lives is considered. We use the bivariate Gompertz (BGP) distribution. This new bivariate survival model and its extension have recently been proposed by Shoaee and Khorram. This model is more flexible and can be applied in the actuarial science of life insurance, and reliability theory in competing risks and shock models. As we know, the maximum likelihood estimates do not always exist and therefore cannot always be calculated. So, the Bayesian estimations are considered using the squared error loss function and a priori distributions that create a dependency between the hyper-parameters. But given the assumptions, one can see that explicit expressions cannot be obtained for Bayesian estimations. The importance sampling method is proposed to calculate the Bayes estimations and also to create the corresponding HPD credible intervals of the unknown parameters. The Monte Carlo simulation studies and analysis of a real data set are also performed to evaluate the proposed method for estimating parameters. Finally, a generalization of the dependent lives models is presented and the Bayesian estimate for this new bivariate family distribution is introduced and a comparison is made between the models of this new family. [ABSTRACT FROM AUTHOR]

Prediction of loss reserves corresponding to dependent lines of business is one of the most important problems in the actuarial sciences. In this paper, we propose a class of copula based multivariate distributions to model the losses with the heavy tailed distribution in the run-off triangles to predict unpaid losses. We set up ANOVA, ANCOVA, and state space models with four choices of copulas, Clayton, Frank, Gumbel, and Gaussian to provide a new procedure for analyzing run-off triangle tables. We use the Hamiltonian Monte Carlo sampler to perform a Bayesian analysis to estimate the parameters. We apply the proposed models to the data set consists of two lines of business of paid losses data from the Schedule P of the National Association of Insurance Commissioners (NAIC) database. Using some well known criteria, we compare the prediction accuracy of the mean models. As a result, the ANCOVA model with the Clayton copula dominates the other models. [ABSTRACT FROM AUTHOR]

Traffic intensity is one of the most critical parameters of single-server Markovian queues. This paper deals with the Bayesian inference for the M/M/1 queue by sampling from the posterior distribution. The No-U-Turn Sampler (NUTS) is a recently developed Markov Chain Monte Carlo (MCMC) algorithm, which is proposed to compute the traffic intensity by observing the number of customers in the system at the departure epoch. Numerical results show that the NUTS outperforms the other algorithms in the literature. [ABSTRACT FROM AUTHOR]

The main purposes of this paper are to derive Bayesian acceptance sampling plans regarding the number of defects per unit of product, and to illustrate how to apply the methodology to the paper pulp industry. The sampling plans are obtained following an economic criterion: minimize the expected total cost of quality. It has been assumed that the number of defects per unit of product follows a Poisson distribution with process average λ, whose prior information is described either for a gamma or for a non- informative distribution. The expected total cost of quality is composed of three independent components: inspection, acceptance and rejection. Both quadratic and step-loss functions have been used to quantify the cost incurred for the acceptance of a lot containing units with defects. Combining the prior information on λ with the loss functions, four different sampling plans are obtained. When the quadratic-loss function is used, an analytical relation between the optimum settings of the sample size and the acceptance number is derived. The robustness analysis indicates that the sampling plans obtained are robust with respect to the prior distribution of the process average as well as to the misspecification of its mean and variance. [ABSTRACT FROM AUTHOR]

The choice of the prior distribution is a key aspect of Bayesian analysis. The spatial Student-t regression model poses some challenges when eliciting priors. It is well known that the propriety of the posterior distribution over objective priors is not always guaranteed, whereas the use of proper prior distributions may dominate and bias the posterior analysis. In this paper, we show the conditions under which our proposed reference prior and the two introduced Jeffreys priors yield to a proper posterior distribution. Simulation studies and a real data application are used in order to evaluate the performance of the reference prior. [ABSTRACT FROM AUTHOR]

The increasing importance of uncertainty quantification in the regulatory evaluation of pharmaceutical products has triggered an explosion of Bayesian methods in recent years. In biomarker discovery and early clinical development, Bayesian methods have established a foothold in developing new drugs, in part due to the increasing availability of greater computational power, often complementing both traditional pharmaceutical statistics and classical statistical methods. In this paper, we present a selective survey of the recent efforts that have been made toward the development and application of effective statistical and computational models in early development statistics. The survey introduces four such case studies and methods that can be used for end-to-end biomarker discovery that includes pre-clinical and early clinical development, from unsupervised clustering to supervised machine learning to modern statistical inference, including but not limited to Bayesian clustering, Bayesian additive regression trees, Bayesian neural networks, and empirical Bayes procedures with the overarching goal of promoting their use and applications among pharmaceutical statisticians. Finally, we present some open issues in Bayesian early clinical methods to help guide the future advancement and wide adoption of Bayesian applications in early clinical pharmaceutical statistics. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Bayesian statistical inference approach for multiple break-points threshold autoregressive moving average model with exogenous inputs (MB-TARMAX) which change in state space and time domain. Based on the appropriate prior information of parameters, we give the full conditional posterior distribution of parameters including the thresholds and break-points. In order to obtain the estimates of parameters, we employ the Markov chain Monte Carlo (MCMC) method via Gibbs sampler with Metropolis-Hastings algorithm. Compared with Metropolis-Hastings algorithm, we apply Hamiltonian Monte Carlo algorithm to avoid the slow space exploration from simple random walk and improve the sampling efficiency. As applications, we demonstrate the effectiveness of our method from simulation experiments and a real example. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic model for predicting the dynamic modulus |E*| of asphalt concrete (AC). A Bayesian Neural Network (BNN) trained on a substantial dataset collected from various states was employed. This approach accounts for the inherent stochasticity in the data generation process (i.e. aleatoric uncertainty) and addresses epistemic or model uncertainty as well. The model successfully predicted dynamic moduli for unseen testing datasets. In practice, predicted moduli could be as accurate and effective as measured values, considering the model uncertainty and |E*| test variability. However, an elevated epistemic uncertainty at extremely low and high |E*| ranges would be expected due to relatively low data points. To enhance interpretability, Shapley Additive Explanations (SHAP) analysis was utilised, demonstrating adherence to physical laws. The primary factors influencing predicted moduli, as identified by the analysis, are temperature, frequency, voids in mineral aggregates, reclaimed asphalt pavement content, and binder low-performance grade. The BNN model was deployed in a web application, making it accessible for application. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]

To better describe the characteristics of time series of counts showing over-dispersion, asymmetry and structural change features, this paper considers a class of random coefficient integer-valued threshold autoregressive processes that properly capture flexible asymmetric and nonlinear responses without assuming the distributions for the errors. By the Taylor expansion, a normally distributed working likelihood is obtained and the Bayesian empirical likelihood inference is conducted for the model parameters. Through the normalized approximation of the nonparametric likelihood, the originally complicated Bayesian calculation has been greatly simplified. Finally, the proposed method is verified via some simulations and an empirical analysis of the meningococcal disease data set in Germany. [ABSTRACT FROM AUTHOR]

Bridge scour is a leading cause of failure for bridges over waterways and is notoriously difficult to detect with accuracy. Dynamic Structural Health Monitoring (SHM) of bridges for scour has gained traction in recent years as monitoring systems have improved and the reliability of measurements increased. Due to the large number of bridges on typical networks and the limited financial resources within asset management agencies, decision-makers must prioritize certain structures when it comes to management in the event of flooding. The decision to install a dynamic SHM system on a bridge must be balanced by the financial benefit of doing so, as limited resources often need to be carefully rationed. In this paper, a methodology is proposed to evaluate such benefit based on the Value of Information (VoI) from Bayesian decision analysis. A case study is presented whereby a dynamic SHM system is considered to be installed on a typical bridge with the aim to support emergency management operations during flooding. The proposed methodology allows computation of the financial benefit of installing a dynamic SHM system over a certain reference period, thus accounting for multiple flood events and scenarios. The various elements of the procedure are discussed in detail. [ABSTRACT FROM AUTHOR]

The determination of plotting positions on probability graph paper so that the associated weighted least squares estimators of the scale parameter and the percentiles of a continuous distribution have certain properties is discussed. Necessary and sufficient conditions are given for an invariant optimal plot for percentile estimation. Also discussed is the derivation of ordered plotting positions. [ABSTRACT FROM AUTHOR]

The article presents discussion of a paper by statistician I.J. Good. The author supposes that most statisticians approaching the problem of testing goodness of fit would have been very much less ambitious than Good has been. Most of the people, if spend some time looking at the first planet, Mercury and the last two, Neptune and Pluto, do not agree with Bode's law, whereas all the intermediate planets from Venus to Uranus seem to fit the law remarkably well. Those statisticians who were sufficiently knowledgeable would presumably meditate on the matters that Good has meditated on; but, having done this, most people would make a merely informal judgment and completely shirk the challenge that Good has met, to evaluate the weight of evidence. Good remarks that if one is going to test goodness of fit of a theory to some data, using either a Neyman-Pearson approach or a Bayesian approach, one have to frame explicit hypotheses. Over a good many years the author has, from time to time tried to think about the general problem of testing goodness of fit, and remain skeptical about whether goodness of fit can be adequately discussed either in Neyrnan-Pearson or in Bayesian terms because of this necessity to have, not only an explicit null hypothesis, but also a fully defined explicit alternative hypothesis or set of alternative hypotheses.

The following paper asks what forms of critical political science are possible given the advent of Machine Learning algorithms like ChatGPT? The technical conditions that provoke this question stem from the fact that such automated systems do not generate identities and representations (as do traditional media with which critical political science conventionally contends) but produce instead rendered outputs based on Bayesian probability calculations. Thus the challenge before us is to theorize criticism vis-a-vis the technical ontologies of the algorithm dispositif. [ABSTRACT FROM AUTHOR]

With the advance of computer-based assessments, many process data, such as response times (RTs), action sequences, Eye-tracking data, the log data for collaborative problem-solving (CPS) and mouse click/drag becomes readily available. Findings from previous studies (e.g., Peng et al., Multivariate Behavioral Research, 1–20, 2021; Xu, The British Journal of Mathematical and Statistical Psychology, 73(3), 474–505, 2020; He & von Davier, Handbook of research on technology tools for real-world skill development (pp. 750–777). IGI Global, 2016; Man & Harring, Educational and Psychological Measurement, 81(3), 441–465, 2021) suggest a substantial relationship between this human–computer interactive process information and proficiency, which means these process data were potentially useful variables for psychological and educational measurement. To make full use of the process data, this paper aims to combine two useful and easily available types of process data, including the mouse click/drag traces and the response times, to the conventional cognitive diagnostic model (CDM) to better understand individual's response behavior and improve the classification accuracy of existing CDM. Then the full Bayesian analysis using Markov chain Monte Carlo (MCMC) was employed to estimate the proposed model parameters. The viability of the proposed model was investigated by an empirical data and two simulation studies. Results indicated the proposed model combing both types of process data could not only improve the attribute classification reliability in real data analysis, but also provide an improvement on item parameters recovery and person classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present the exact distribution of the sum of two independent Topp-Leone variables which becomes useful in calculating the exact reliability of a standby system with Topp-Leone distributed component units when the values of parameters are known. Applications in Bayesian analysis with binomial sampling are also considered, and the comparison of the performance of Beta and Topp-Leone priors is then made. [ABSTRACT FROM AUTHOR]

Two types of outliers that may occur in data are considered in this paper: additive outliers (AO) and innovational outliers (IO). We have generalized the two types of contaminations AO and IO to the multiple case for an autoregressive model of order p with a regression trend. We adopt the Bayesian approach combined with Gibbs sampling to jointly estimate the model parameters and the outliers on the first hand, and on the other hand we use a test based on p-values and other discrimination Bayesian criteria to detect the location and the magnitude of the two types of outliers. An intensive simulation study is presented for illustrating the performance of the method relative to maximum likelihood estimation, mainly for small sample sizes. Our method is applied to a real data set. [ABSTRACT FROM AUTHOR]

System models based on Bayesian networks (BNs) are widely applied in different areas. This paper facilitates the use of such models by advancing the ranked nodes method (RNM) for constructing conditional probability tables (CPTs) of BNs by expert elicitation. In RNM, the CPT of a child node is generated using a function known as the weight expression and weights of parent nodes that are elicited from the expert. However, there is a lack of exact guidelines for eliciting these parameters which complicates the use of RNM. To mitigate this issue, this paper introduces a novel framework for supporting the RNM parameter elicitation. First, the expert assesses the two most probable states of the child node in scenarios that correspond to extreme states of the parent nodes. Then, a feasible weight expression and a feasible weight set are computationally determined. Finally, the expert selects weight values from this set. [ABSTRACT FROM AUTHOR]

Most European Administrative Networks (EANs) have been operational for over two decades. Yet, our understanding of their evolution is limited. How do EANs evolve? I formulate two hypotheses, premised on the notion that networks help actors address collective action problems by establishing social capital, whose distribution in the network changes over time to respond to changed circumstances. Using a Bayesian network model of the structure of the Council of European Energy Regulators (CEER) at two-time points (1998 and 2014), this paper shows that regulatory relationships shifted from being focused on learning to be aimed at achieving compromise in order to influence European policy-making. Rather than functional, static devices of rule harmonization, EANs are dynamic social networks that adapt to their changing environment. The paper provides a theoretical background and empirical approach to understand and analyse EANs evolution in different sectors. [ABSTRACT FROM AUTHOR]

Reconstruction of the Gene Regulatory Network (GRN) is important in understanding the functionalities of a living cell. The reconstruction of GRN is based on microarray gene expression data. In this paper, Bayesian Network (BN) model is used for GRN reconstruction. Here, to train BN-modelled GRN from gene expression data, four meta-heuristic techniques, namely, Simulated Annealing, Tabu Simulated Annealing, Binary Particle Swarm Optimisation (BPSO), and Binary Differential Evolution (BDE) have been applied. The algorithms have been executed on two synthetic datasets and two real datasets. The comparison of their performances demonstrates that BDE- and BPSO-based algorithms are superior to other algorithms. These two efficient algorithms have outperformed the state-of-the-art methods in consideration of F1 score. [ABSTRACT FROM AUTHOR]

Network meta-analysis is an extension of standard meta-analysis. It allows researchers to build a network of evidence to compare multiple interventions that may have not been compared directly in existing publications. With a Bayesian approach, network meta-analysis can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows for the estimation of relative treatment effects to quantify the uncertainty of parameter estimates, and to rank all the treatments in the network. Ranking treatments using both direct and indirect evidence can provide guidance to policy makers and clinicians for making decisions. The purpose of this paper is to introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences. We discuss several essential concepts of BNMA, including the assumptions of homogeneity and consistency, the fixed and random effects models, prior specification, and model fit evaluation strategies, while pointing out some issues and areas where researchers should use caution in the application of BNMA. Additionally, using an automated R package, we provide a step-by-step demonstration on how to conduct and report the findings of BNMA with a real dataset of psychological interventions extracted from PubMed. [ABSTRACT FROM AUTHOR]

The basic objective of this paper is to introduce a new parametrization and extension of stochastic volatility models where the diffusion of the stock return is generated by the shifted compound Poisson distribution. This new class generalizes the affine standard process and can be better adapted to several empirical characteristics of return data. The shifted compound Poisson Model is more flexible and is usually implemented to modeling negative and non negative continuous data with a discrete probability mass at zero. The proposed model parameters, based on the Bayesian analysis, have been estimated. Furthermore, the dispersion stochastic volatility ϕ t which follows an infinite mixture inverse Gamma distribution is elaborated. The model's performance has been analyzed with real data for its selection by varying the power parameter p in { 0 } ∪ (1 , 2). The paper also develops formal tools for comparing the basic standard stochastic volatility and the proposed model. Moreover, the proposed methodology is illustrated by analyzing four series of real indexes returns. [ABSTRACT FROM AUTHOR]

The fundamental problem in finite population sampling lies in the prediction of a function of the unobserved data based on the observed data. The original focus on the prediction of the finite population quantities has been supplemented recently by the use of statistical models. To this end, this paper uses a model-based inference and applies the Bayesian, robust Bayesian and frequentist approaches. Several robust Bayesian analysis criteria can be used for this purpose including the posterior regret gamma-minimax (PRGM), conditional gamma-minimax (CGM), and the most stable (MS). The aim of this paper is a comparison of the Bayesian, robust Bayesian and frequentist predictors of a general linear combination of the finite population values under a superpopulation model, which seems to apply in many practical problems, and several loss functions. Finally, a real-life example is provided to predict a finite population mean and compare the estimated risk and bias of the obtained predictors under the squared error (SE), linear-exponential (LINEX) and reflected normal (RN) loss functions by a simulation study. [ABSTRACT FROM AUTHOR]

In this paper we propose an objective Bayesian method to analyze the accelerated degradation model based on the new Wiener process stated in Ye, Chen, and Shen (Reliab. Eng. Syst. Safe., 2015, 139: 58–67). The Jeffreys prior and reference priors are derived after reparameterization, and the posterior properties under each prior are validated. A Metropolis random walk algorithm is then employed to generate samples from the posterior distributions, and a simulation study is carried out to reveal the frequentist properties of these priors. Finally, we apply the approach to a real data and obtain the mean-time-to-failure (MTTF) under the usage condition. [ABSTRACT FROM AUTHOR]

Cultural interaction and exchange across the Bering Strait of northern Alaska played a central role in the emergence of Arctic maritime adaptations. Yet poor chronological control limits our ability to explore processes of cultural change over the last 5000 years. We address this problem by synthesizing the available radiocarbon record for the region, carrying out Bayesian analysis of a regional radiocarbon database, and analyzing the BAR-1 (Birnirk) site using new dates published in this paper. Our synthesis and our illustrative analysis of the BAR-1 site highlights several intriguing temporal and spatial trends with implications for interaction between cultural groups. Our analysis also underscores the uncertainty associated with dating cultural phases and identifies specific areas where additional research is needed to further our understanding of cultural interaction in this complex region. [ABSTRACT FROM AUTHOR]

Process capability indices such as Cp, CpL, CpU, and Cpk have been used for evaluating the performance of a manufacturing process in statistical quality control and statistical process control fields. In this paper, a Bayes estimator of Cpk is constructed such that the prior mean is set to be equal to a specified value while the prior distribution is weakly informative. This estimator is compared with conventional Bayes and non Bayes estimators. [ABSTRACT FROM AUTHOR]

Measurement error affects the quality of population orderings of an index and, hence, increases the misclassification of the poor and the non-poor groups and affects statistical inferences from binary regression models. Hence, the conclusions about the extent, profile, and distribution of poverty are likely to be misleading. However, the size and type (false positive/negatives) of classification error have remained untraceable in poverty research. This paper draws upon previous theoretical literature to develop a Bayesian-based estimator of population misclassification and binary-regression coefficient bias. The study uses the reliability values of existing poverty indices to set up a Monte Carlo study based on factor mixture models to illustrate the connections between measurement error, misclassification, and bias and evaluate the procedure and discusses its importance for real-world applications. [ABSTRACT FROM AUTHOR]

The solution of the Montmort's matching problem can be seen as the probability mass function of the number of matches in an experiment for assessing the agreement between nominal variables and gold standard classifications. [Vidal I, de Castro M. A Bayesian analysis of the matching problem. J Stat Plan Inference. 2021;212:194–200] presented a generalization of the Montmort's matching problem by considering the chronological order in what assignments are made and counting the number of unforced errors additionally to the number of matches. These authors carried out a Bayesian analysis of the problem, but in this paper we found a solution of this new approach from a frequentist point of view. We found the bivariate probability mass function of the number of matches and the number of unforced errors. The marginal distribution of the number of unforced errors is computed and expressed in terms of the Stirling numbers of the second kind. Also, some elementary properties were proven, including that the distribution of the unforced errors is equal to that of forced errors. As a practical consequence, we propose a new experiment and a new way of collecting and analysing the data in order to assess the agreement between nominal variables and gold standard classifications. Two real data sets were analysed using the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, we make use of meta-elliptical copula functions to build a new multivariate distribution with fixed marginal distributions and dependence structure to analyze bounded data. Specifically, we present a flexible p-elliptical multivariate probability distribution in the hypercube (0 , 1) p p with fixed marginal GF-quantile distributions. We then present some illustrative examples and a meta-elliptical multivariate regression model as a flexible alternative to the multivariate normal regression model on unit intervals. A simulation study and real-life data analysis using a Bayesian framework with the extreme-value quantile functions show the flexibility of the proposed meta-multivariate normal regression model for modeling the observed proportion response variables. [ABSTRACT FROM AUTHOR]

Cognitive diagnostic assessment based on Bayesian networks (BN) is developed in this paper to evaluate student understanding of the physical concept of buoyancy. we propose a three-order granular-hierarchy BN model which accounts for both fine-grained attributes and high-level proficiencies. Conditional independence in the BN structure is tested and utilized to validate the proposed model. The proficiency relationships are verified and the initial Q-matrix is refined. Then, an optimized granular hierarchy model is constructed based on the updated Q-matrix. All variants of the constructed models are evaluated on the basis of the prediction accuracy and the goodness-of-fit test. The experimental results demonstrate that the optimized granular-hierarchy model has the best prediction and model-fitting performance. In general, the BN method not only can provide more flexible modeling approach, but also can help validate or refine the proficiency model and the Q-matrix and this method has its unique advantage in cognitive diagnosis. [ABSTRACT FROM AUTHOR]

Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged n -Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts. [ABSTRACT FROM AUTHOR]

Hypothesis testing is an essential statistical method in experimental psychology and the cognitive sciences. The problems of traditional null hypothesis significance testing (NHST) have been discussed widely, and among the proposed solutions to the replication problems caused by the inappropriate use of significance tests and p-values is a shift toward Bayesian data analysis. However, Bayesian hypothesis testing is concerned with various posterior indices for significance and the size of an effect. This complicates Bayesian hypothesis testing in practice, as the availability of multiple Bayesian alternatives to the traditional p-value causes confusion which one to select and why. In this paper, various Bayesian posterior indices which have been proposed in the literature are compared and their benefits and limitations are discussed. The comparison shows that conceptually not all proposed Bayesian alternatives to NHST and p-values are beneficial, and the usefulness of some indices strongly depends on the study design and research goal. However, the comparison also reveals that there exist at least two candidates among the available Bayesian posterior indices which have appealing theoretical properties and are widely underused in the cognitive sciences. [ABSTRACT FROM AUTHOR]

Human and organizational factors are the contributing factors for collision accidents from the historical data. To discover the key influencing factor, a human factor analysis and classification system based Bayesian Network model is proposed in this paper. The kernel of this proposed model is first to derive the unsafe acts from the perspective of perception, decision-making, and execution failures using the collision avoidance scheme, to classify the human factors into five categories using the modified human-factor analysis and classification system, and to transform the influencing factors of HOFs in the modified HFACS into the graphical structure of the Bayesian network. The results are verified from historical collision accidents data in the Yangtze River, and sensitivity analysis is carried out to validate the axioms of the Bayesian network. From further analysis, the causation factor and global causation chain of ship collision accidents can be derived. Consequently, the results are beneficial for the prevention and control of ship collision accidents in the Yangtze River by reducing human and organization factors. [ABSTRACT FROM AUTHOR]

Aiming at the structural learning problem of the additive noise model in causal discovery and the challenge of massive data processing in the era of artificial intelligence, this paper combines partial rank correlation coefficients and proposes two new Bayesian network causal structure learning algorithms: PRCB algorithm based on threshold selection and PRCS algorithm based on hypothesis testing. We mainly made three contributions. First, we proved that the partial rank correlation coefficient can be used as the standard of independence test, and explored the distribution of corresponding statistics. Second, the partial rank correlation coefficient is associated with the correlation, and a causal discovery algorithm PRCB based on partial rank correlation and an improved PRCS algorithm based on hypothesis testing are proposed. Finally, comparing with the existing technology on seven classic Bayesian networks, it proves the superiority of the algorithm in low-dimensional networks; the processing of millions of data on three high- dimensional Bayesian networks verifies the high-efficiency performance of the algorithm in high-dimensional large sample data; the application performance of the algorithm is tested by performing fault prediction on the real power plant equipment measurement point data set. Theoretical analysis and experimental results have proved the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Currently, deep learning methods have been widely applied to many fields like classification. Generally, these methods use the technology like transferring to make a model work well on different domains like building a strong brain. Existing transferring methods include complex model reconstruction or high-quality retraining on the new domains that makes it hard to implement or ensure high accuracy. This paper introduces a domain-model-based Bayesian network and related solutions to solve this problem. Our solutions make it easier to add new domains while ensure high accuracy like a flexible brain. The experimental results show that our solutions can ensure higher accuracy than the single model one. Furthermore, we also evaluated the network in transferring case and the result shows that the accuracy of our solutions is higher than the single transferred model. [ABSTRACT FROM AUTHOR]