Stats, Volume 8, Issue 3

This article introduces a flexible time series regression model known as the Mixture of Integer-Valued Generalized Autoregressive Conditional Heteroscedasticity (MINGARCH). Mixture models provide versatile frameworks for capturing heterogeneity in co...

In repeated-measures meta-analyses, raw data are often unavailable, preventing the calculation of the correlation coefficient r between pre- and post-intervention values. As a workaround, many researchers adopt a heuristic approximation of r = 0.7. H...

In many complex applications, both data heterogeneity and homogeneity are present simultaneously. Overlooking either aspect can lead to misleading statistical inferences. Moreover, the increasing prevalence of complex, non-Euclidean data calls for mo...

Penalized regression methods are widely used for variable selection. Non-negative garrote (NNG) was one of the earliest methods to combine variable selection with shrinkage of regression coefficients, followed by lasso. About a decade after the intro...

Many applications in health research involve the analysis of multivariate distributions of random variables. In this paper, we review the basic theory of copulas to illustrate their advantages in deriving a joint distribution from given marginal dist...

This paper presents the implementation of a language model–based strategy for the automatic codification of crime narratives for the production of official statistics. To address the high workload and inconsistencies associated with manual codi...

This paper suggests a theoretical framework to summarise the empirical literature on the relationships between sports and both religious and secular ethics, and it suggests two interrelated theoretical models to empirically evaluate the extent to whi...

In observational causal inference studies, unmeasured confounding remains a critical threat to the validity of effect estimates. While proximal causal inference (PCI) has emerged as a powerful framework for mitigating such bias through proxy variable...

This paper reveals a simple methodology to create local-correlation models suitable for the closed-form pricing of two-asset financial derivatives. The multivariate models are built to ensure two conditions. First, marginals follow desirable processe...

The need to improve medical diagnosis is of utmost importance in medical research, consisting of the optimization of accurate classification models able to assist clinical decisions. To minimize the errors that can be caused by using a single classif...