Stats

Large-scale imaging studies often face challenges stemming from heterogeneity arising from differences in geographic location, instrumental setups, image acquisition protocols, study design, and latent variables that remain undisclosed. While numerous regression models have been developed to elucidate the interplay between imaging responses and relevant covariates, limited attention has been devoted to cases where the imaging responses pertain to the domain of shape. This adds complexity to the problem of imaging heterogeneity, primarily due to the unique properties inherent to shape representations, including nonlinearity, high-dimensionality, and the intricacies of quotient space geometry. To tackle this intricate issue, we propose a novel approach: a shape-on-scalar regression model that incorporates confounder adjustment. In particular, we leverage the square root velocity function to extract elastic shape representations which are embedded within the linear Hilbert space of square integrable functions. Subsequently, we introduce a shape regression model aimed at characterizing the intricate relationship between elastic shapes and covariates of interest, all while effectively managing the challenges posed by imaging heterogeneity. We develop comprehensive procedures for estimating and making inferences about the unknown model parameters. Through real-data analysis, our method demonstrates its superiority in terms of estimation accuracy when compared to existing approaches. Full article

This study aimed to perform a bibliometric analysis of terroir and explore its conceptual horizons. Advancements in terroir research until 2022 were investigated using the Scopus database, R, and VOSviewer. Out of the 907 results, the most prevalent document types were articles (771) and reviews (70). The annual growth rate of published manuscripts in this field was 7.8%. The research on terroir encompassed a wide range of disciplines, with significant contributions from Agricultural and Biological Sciences, Social Sciences, Environmental Science, Biochemistry, Genetics, and Molecular Biology. Through keyword analysis, the study identified the most frequently occurring terms in titles, abstracts, and keywords fields, including ‘terroir’, ‘wine’, ‘soil’, ‘wines’, ‘grape’, ‘analysis’, ‘vineyard’, ‘composition’, and ‘climate’. A trend topic analysis revealed that research in terroir primarily focused on the geo-ecology and physiology of grapes. Furthermore, considerable attention was given to methods and techniques related to the physicochemical, sensory, and microbial characterization of terroir and various aspects of the wine industry. Initially, the research in this domain was focused on terroir, authenticity, grapevine, soils, soil moisture, and wine quality. However, over time, the research agenda expanded to include topics such as food analysis, viticulture, wine, taste, sustainability, and climate change. New research areas emerged, including phenolic compounds, anthocyanin, phenols, sensory analysis, and precision agriculture—all of which became integral components of the scientific studies on terroir. Overall, this study provided valuable insights into the historical trends and current developments in terroir research, contributing to our understanding of the frontiers in this field. Full article

This paper considers the construction of a family of discrete distributions with the flexibility to cater for under-, equi- and over-dispersion in count data using a finite mixture model based on standard distributions. We are motivated to introduce this family because its simple finite mixture structure adds flexibility and facilitates application and use in analysis. The family of distributions is exemplified using a mixture of negative binomial and shifted negative binomial distributions. Some basic and probabilistic properties are derived. We perform hypothesis testing for equi-dispersion and simulation studies of their power and consider parameter estimation via maximum likelihood and probability-generating-function-based methods. The utility of the distributions is illustrated via their application to real biological data sets exhibiting under-, equi- and over-dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM–Poisson distributions for handling under-, equi- and over-dispersion in count data. Full article

COVID-19 spread dramatically across the world in the beginning of 2020. This paper presents a novel alert system that will detect abrupt changes in the COVID-19 or other pandemic incidence rate through the estimated time-varying reproduction number (Rt). We applied the system to detect abrupt changes in the COVID-19 pandemic incidence rates in thirteen world regions with eight in the US and five across the world. Subsequently, we also evaluated the system with the 2009 H1N1 pandemic in Hong Kong. Our system performs well in detecting both the abrupt increases and decreases. Users of the system can obtain accurate information on the changing trend of the pandemic to avoid being misled by low incidence numbers. The world may face other threatening pandemics in the future; therefore, it is crucial to have a reliable alert system to detect impending abrupt changes in the daily incidence rates. An added benefit of the system is its ability to detect the emergence of viral mutations, as different virus strains are likely to have different infection rates. Full article

In multifactor fixed-effects ANOVAs, we show how to construct orthonormal F contrasts for main effects. Our primary focus is the case when the levels of the factor of interest are ordered. Likewise, in multifactor equally replicated fixed-effects ANOVAs, we show how to construct orthonormal F contrasts for interactions. The primary focus here is on interactions when both factors are ordered, although the approach also applies if just one factor is ordered. Interactions with both factors ordered may be interpreted in terms of generalised correlations. Full article

The rise of explainable natural language processing spurred a bulk of work on datasets augmented with human explanations, as well as technical approaches to leverage them. Notably, generative large language models offer new possibilities, as they can output a prediction as well as an explanation in natural language. This work investigates the capabilities of fine-tuned text-to-text transfer Transformer (T5) models for commonsense reasoning and explanation generation. Our experiments suggest that while self-rationalizing models achieve interesting results, a significant gap remains: classifiers consistently outperformed self-rationalizing models, and a substantial fraction of model-generated explanations are not valid. Furthermore, training with expressive free-text explanations substantially altered the inner representation of the model, suggesting that they supplied additional information and may bridge the knowledge gap. Our code is publicly available, and the experiments were run on open-access datasets, hence allowing full reproducibility. Full article

This study considers the statistical estimation of relations presented by implicit functions. Such structures define mutual interconnections of variables rather than outcome variable dependence by predictor variables considered in regular regression analysis. For a simple case of two variables, pairwise regression modeling produces two different lines of each variable dependence using another variable, but building an implicit relation yields one invertible model composed of two simple regressions. Modeling an implicit linear relation for multiple variables can be expressed as a generalized eigenproblem of the covariance matrix of the variables in the metric of the covariance matrix of their errors. For unknown errors, this work describes their estimation by the residual errors of each variable in its regression by the other predictors. Then, the generalized eigenproblem can be reduced to the diagonalization of a special matrix built from the variables’ covariance matrix and its inversion. Numerical examples demonstrate the eigenvector solution’s good properties for building a unique equation of the relations between all variables. The proposed approach can be useful in practical regression modeling with all variables containing unobserved errors, which is a common situation for the applied problems. Full article

Global scenarios of organizations show investments wasted in projects with poor performances in more than 11 percent of cases, according to the Project Management Institute. This research aims to guide organizations in assertively investing in the right pertinent factors to improve project success rates and speed up project management maturity at a higher accuracy level using statistical predictions. Challenging existing drivers for project management maturity models and expanding their current practical view will be the result of a quantitative methodology based on a survey supported by data collection targeting the project management community in Brazil. The originality and value of this research are in contributing to the development of new project maturity models statistically supported by the increasing rate of maturity accuracy, which can be continually improved by confident data input into the model. The results show a high correlation between the performance measurement system and the project success rate associated with project management maturity. In addition, this research contemplates the relationship between organizational culture, business type, and project management office and project management maturity. Full article

To address the difficult problem of the multi-step-ahead prediction of nonparametric autoregressions, we consider a forward bootstrap approach. Employing a local constant estimator, we can analyze a general type of nonparametric time-series model and show that the proposed point predictions are consistent with the true optimal predictor. We construct a quantile prediction interval that is asymptotically valid. Moreover, using a debiasing technique, we can asymptotically approximate the distribution of multi-step-ahead nonparametric estimation by the bootstrap. As a result, we can build bootstrap prediction intervals that are pertinent, i.e., can capture the model estimation variability, thus improving the standard quantile prediction intervals. Simulation studies are presented to illustrate the performance of our point predictions and pertinent prediction intervals for finite samples. Full article

In surveys requiring cost efficiency, such as medical research, measuring the variable of interest (e.g., disease status) is expensive and/or time-consuming; however, we often have access to easily obtainable characteristics about sampling units. These characteristics are not typically employed in the data collection process. Judgment post-stratification (JPS) sampling enables us to supplement the random samples from the population of interest with these characteristics as ranking information. This paper develops methods based on the JPS samples for estimating categorical ordinal populations. We develop various estimators from the JPS data even for situations where the JPS suffers from empty strata. We also propose the JPS estimators using multiple ranking resources. Through extensive numerical studies, we evaluate the performance of the methods in estimating the population. Finally, the developed estimation methods are applied to bone mineral data to estimate the bone disorder status of women aged 50 and older. Full article

In the present paper, a new special function, the so-called extreme value $\mathbb{H}$-function, is introduced. This new function, which is a generalization of the H-function with a particular set of parameters, appears while dealing with products and quotients of a wide class of extreme value random variables. Some properties, special cases and a series representation are provided. Some statistical applications are also briefly discussed. Full article

In this study, we introduce a new generalized family of distributions called the Exponentiated Half Logistic-Harris-G (EHL-Harris-G) distribution, which extends the Harris-G distribution. The motivation for introducing this generalized family of distributions lies in its ability to overcome the limitations of previous families, enhance flexibility, improve tail behavior, provide better statistical properties and find applications in several fields. Several statistical properties, including hazard rate function, quantile function, moments, moments of residual life, distribution of the order statistics and Rényi entropy are discussed. Risk measures, such as value at risk, tail value at risk, tail variance and tail variance premium, are also derived and studied. To estimate the parameters of the EHL-Harris-G family of distributions, the following six different estimation approaches are used: maximum likelihood (MLE), least-squares (LS), weighted least-squares (WLS), maximum product spacing (MPS), Cramér–von Mises (CVM), and Anderson–Darling (AD). The Monte Carlo simulation results for EHL-Harris-Weibull (EHL-Harris-W) show that the MLE method allows us to obtain better estimates, followed by WLS and then AD. Finally, we show that the EHL-Harris-W distribution is superior to some other equi-parameter non-nested models in the literature, by fitting it to two real-life data sets from different disciplines. Full article

The Boltzmann–Gibbs–Shannon (BGS) entropy is the only entropy form satisfying four conditions known as Khinchin’s axioms. The uniqueness theorem of the BGS entropy, plus the fact that Shannon’s mutual information completely characterizes independence between the two underlying random elements, puts the BGS entropy in a special place in many fields of study. In this article, the fourth axiom is replaced by a slightly weakened condition: an entropy whose associated mutual information is zero if and only if the two underlying random elements are independent. Under the weaker fourth axiom, other forms of entropy are sought by way of escort transformations. Two main results are reported in this article. First, there are many entropies other than the BGS entropy satisfying the weaker condition, yet retaining all the desirable utilities of the BGS entropy. Second, by way of escort transformations, the newly identified entropies are the only ones satisfying the weaker axioms. Full article

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment. Full article

There are given characterizations of the exponential distribution based on the properties of independence of linear forms with random coefficients. Results based on the constancy of regression of one statistic in a linear form are obtained. Related characterizations based on the property of the identical distribution of statistics are also provided. Full article

The use of large lecture halls in business and economic education often dictates the use of multiple-choice exams to measure student learning. This study asserts that student performance on these types of exams can be viewed as the result of the process of elimination of incorrect answers, rather than the selection of the correct answer. More specifically, how students respond on a multiple-choice test can be broken down into the fractions of questions where no wrong answers can be eliminated (i.e., random guessing), one wrong answer can be eliminated, two wrong answers can be eliminated, and all wrong answers can be eliminated. The results from an empirical model, representing a mixture of binomials in which the probability of a correct choice depends on the number of incorrect choices eliminated, we find, using student performance data from a final exam in principles of microeconomics consisting of 100 multiple choice questions, that the responses to all of the questions on the exam can be characterized by some form of guessing, with more than 26 percent of questions being completed using purely random guessing. Full article

The new Ristić and Balakhrisnan or Gamma-Topp-Leone-Type II-Exponentiated Half Logistic-G (RB-TL-TII-EHL-G) family of distributions is introduced and investigated in this paper. This work derives and studies some of the main statistical characteristics of this new family of distributions. The maximum likelihood estimation technique is used to estimate the model parameters, and a simulation study is used to assess the consistency of the estimators. Applications to three real-life datasets from various fields show the value and adaptability of the new RB-TL-TII-EHL-G family of distributions. From our results, it is evident that the new proposed distribution is flexible enough to characterize datasets from different fields compared to several other existing distributions in the literature. Full article

Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent. In many cases, researchers nevertheless intend to work with a misspecified target model of interest. In this article, a simultaneous statistical inference for sampling errors and model misspecification errors is discussed. A modified formula for the variance matrix of the parameter estimate is obtained by imposing a stochastic model for model errors and applying M-estimation theory. The presence of model errors is quantified in increased standard errors in parameter estimates. The proposed inference is illustrated with several analytical examples and an empirical application. Full article

Researchers conducting longitudinal data analysis in psychology and the behavioral sciences have several statistical methods to choose from, most of which either require specialized software to conduct or advanced knowledge of statistical methods to inform the selection of the correct model options (e.g., correlation structure). One simple alternative to conventional longitudinal data analysis methods is to calculate the area under the curve (AUC) from repeated measures and then use this new variable in one’s model. The present study assessed the relative efficacy of two AUC measures: the AUC with respect to the ground (AUC-g) and the AUC with respect to the increase (AUC-i) in comparison to latent growth curve modeling (LGCM), a popular repeated measures data analysis method. Using data from the ongoing Panel Study of Income Dynamics (PSID), we assessed the effects of four predictor variables on repeated measures of social anxiety, using both the AUC and LGCM. We used the full information maximum likelihood (FIML) method to account for missing data in LGCM and multiple imputation to account for missing data in the calculation of both AUC measures. Extracting parameter estimates from these models, we next conducted Monte Carlo simulations to assess the parameter bias and power (two estimates of performance) of both methods in the same models, with sample sizes ranging from 741 to 50. The results using both AUC measures in the initial models paralleled those of LGCM, particularly with respect to the LGCM baseline. With respect to the simulations, both AUC measures preformed as well or even better than LGCM in all sample sizes assessed. These results suggest that the AUC may be a viable alternative to LGCM, especially for researchers with less access to the specialized software necessary to conduct LGCM. Full article