2025 September - 35 articles
The augmented logistic regression proposed by Diaz-Quijano directly provides risk ratios with an augmented dataset with the pseudo-observations. However, the standard errors of regression coefficients cannot be accurately estimated using either the o...
One of the most popular cure rate models in the literature is the Berkson and Gage mixture model. A characteristic of this model is that it considers the cure to be a latent event. However, there are situations in which the cure is well known, and th...
This paper introduces the Unit-Modified Weibull (UMW) distribution, a novel probability model defined on the unit interval
Literature review plays a crucial role in research. This paper explores bibliometrics, which utilize statistical tools to evaluate the researcher’s scientific contributions. Its intent is to map frequently cited articles and authors, identify t...
In this study, we examine the presence of bias in weighted least squares (WLS) estimation within the context of first-order bifurcating autoregressive (BAR(1)) models. These models are widely used in the analysis of binary tree-structured data, parti...
A standard approach for assessing the performance of partition models is to create synthetic datasets with a prespecified clustering structure and assess how well the model reveals this structure. A common format involves subjects being assigned to d...
The phase-type aging model (PTAM) belongs to a class of Coxian-type Markovian models that can provide a quantitative description of well-known aging characteristics that are part of a genetically determined, progressive, and irreversible process. Due...
This paper describes a region-oriented method of pattern classification based on the Cartesian system model (CSM), a mathematical model that allows manipulating mixed feature-type symbolic data. We use the supervised hierarchical conceptual clusterin...
The aim of this research is to provide a 25-year multi-model analysis of gender dynamics in economics articles that include at least one Romanian-affiliated author, published in Web of Science journals between 2000 and 2025 (2025 records current as o...
Change-point models are frequently considered when modeling phenomena where a regime shift occurs at an unknown time. In aging research, these models are commonly adopted to estimate of the onset of cognitive decline. Yet these models present several...
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...
The increasing complexity of financial reporting has enabled the implementation of innovative accounting practices that often obscure a company’s actual performance. This project seeks to uncover manipulative behaviours by constructing an anoma...
We propose a new unit distribution, study its properties, and provide an important application in the field of geology through a set of risk indicators. We test its practicality through two applications to real data, make comparisons with the well-kn...
Standard asymptotic inference for proportional hazards models is conventionally performed by calculating a standard error for the estimate and comparing the estimate divided by the standard error to a standard normal distribution. In this paper, we c...
High-dimensional statistics are used when
This article proposes an extension of detection methods for the Tobit model by generalizing existing approaches from cases with known parameters to more realistic scenarios where the parameters are unknown. The main objective is to develop detection...
We propose a robust linear regression model assuming a log-skew-t distribution for the response variable, with the aim of exploring the association between the covariates and the quantiles of a continuous and positive response variable under skewness...
The discovery of proteomic biomarkers in cancer research can be effectively performed in situ by exploiting Matrix-Assisted Laser Desorption Ionization (MALDI) Mass Spectrometry Imaging (MSI). However, due to experimental limitations, the spectra ext...
Testing interval hypotheses is of huge relevance in the biomedical and cognitive sciences; for example, in clinical trials. Frequentist approaches include the proposal of equivalence tests, which have been used to study if there is a predetermined me...
The problem of estimating the ratio of the means of a two-component Poisson mixture model is considered, when each component is subject to zero-inflation, i.e., excess zero counts. The resulting zero-inflated Poisson mixture (ZIPM) model can be viewe...
The diagnosis-related group (DRG) system classifies patients into different groups in order to facilitate decisions regarding medical insurance payments. Currently, more than 600 standard DRGs exist in China. Payment details represented by DRG weight...
Imbalanced regression poses a significant challenge in real-world prediction tasks, where rare target values are prone to overfitting during model training. To address this, prior research has employed relevance functions to quantify the rarity of ta...
We consider the noise-induced transport of overdamped Brownian particles in a ratchet system driven by nonequilibrium symmetric three-level Markovian noise and additive white noise. In addition to a detailed analysis of this system, we consider a sim...
Bayesian methods have attracted increasing interest in the design and analysis of clinical trials. Many of these clinical trials investigate time-to-event endpoints. The Weibull distribution is often used in survival and reliability analysis to model...
This paper characterises Ethicametrics (EM) as a new interdisciplinary scientific research area focusing on metrics of ethics (MOE) and ethics of metrics (EOM), by providing a comprehensive methodological framework. EM is scientific: it is based on b...
Traditional methods for mission reliability assessment under operational testing conditions exhibit some limitations. They include coarse modeling granularity, significant parameter estimation biases, and inadequate adaptability for handling heteroge...
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