2025 December - 36 articles
In the presence of multicollinearity, the ordinary least squares (OLS) estimators, aside from BLUE (best linear unbiased estimator), lose efficiency and fail to achieve minimum variance. In addition, these estimators are highly sensitive to outliers...
In this work, we develop and investigate statistical extensions of gauge integrability and gauge summability for double sequences of functions of two real variables, formulated within the framework of deferred weighted means. We begin by establishing...
The asymptotic limit theorems of information and control theories, instantiated as the Rate Distortion Control Theory of bounded rationality, enable examination of stability across models of cognition based on a variety of fundamental, underlying pro...
This article provides a critical assessment of the Birnbaum–Saunders (BS) distribution, a pivotal statistical model for lifetime data analysis and reliability estimation, particularly in fatigue contexts. The model has seen successfully applied...
The optimal allocation of funds within a portfolio is a central research focus in finance. Conventional mean-variance models often concentrate a significant portion of funds in a limited number of high-risk assets. To promote diversification, Shannon...
Background: Health disparities research increasingly relies on complex survey data to understand survival differences between population subgroups. While Peters–Belson decomposition provides a principled framework for distinguishing disparities...
The paper shows an alternative perspective of the reduced chi-square as a measure of the goodness of fitting methods. The reduced chi-square is given by the ratio of the fitting over the propagation errors, that is, a universal relationship that hold...
This article presents biplots derived from factor analysis of correlation matrices for both continuous and ordinal data. It introduces biplots specifically designed for factor analysis, detailing the geometric interpretation for each data type and pr...
We developed a class of bivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and Negative...
In an infinite-/super-population (SP) setup, regression analysis of longitudinal data, which involves repeated responses and covariates collected from a sample of independent individuals or correlated individuals belonging to a cluster such as a hous...
There are many studies in the scientific literature that present predictions from parametric statistical models based on maximum likelihood estimates of the unknown parameters. However, generating predictions from maximum likelihood parameter estimat...
In this work, we present a robust examination of the Bayesian estimators utilizing the two-parameter Upper truncated XLindley model, a unique Lindley model variant, and the oscillation of posterior risks. We provide the model in a censored scheme alo...
Consider regression models where the response variable Y only depends on the
Lung cancer remains one of the leading causes of death worldwide due to its high rates of illness and mortality. In this study, we applied a continuous-time multi-state Markov model to examine how lung cancer progresses through six clinically defined...
Dimensionality reduction is fundamental for analyzing high-dimensional data, supporting visualization, denoising, and structure discovery. We present a systematic, large-scale benchmark of three widely used methods—Principal Component Analysis...
In addition to products with a single component, this study examines products composed of multiple components whose lifetimes follow a one-parameter exponential distribution. An overall lifetime performance index is developed to assess products under...
In this work, we introduce a statistical framework for monitoring the performance of a breakwater structure in reducing wave impact. The proposed methodology aims to achieve diligent tracking of the underlying process and the swift detection of any p...
Datasets with many zero outcomes are common in real-world studies and often exhibit overdispersion and strong correlations among predictors, creating challenges for standard count models. Traditional approaches such as the Zero-Inflated Poisson (ZIP)...
Receiver operating characteristic (ROC) surfaces provide a natural extension of ROC curves to three-class diagnostic problems. A key summary index is the volume under the surface (VUS), representing the probability that a randomly chosen observation...
The use of probability distribution functions in deriving synthetic hydrographs has become a robust method for modeling the response of watersheds to precipitation events. This approach leverages statistical distributions to capture the temporal stru...
Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above probl...
Conditioning is a very useful way of using correlated information to reduce the variability of an estimate. Conditioning an estimate on a correlated estimate, reduces its covariance, and so provides more precise inference than using an unconditioned...
Goodness-of-fit testing remains a fundamental problem in statistical inference with broad practical importance. In this paper, we introduce two new goodness-of-fit tests grounded in entropy-based density estimation techniques. The first is a boundary...
The primary objective of robust parameter design (RPD) is to determine the optimal settings of control factors in a system to minimize response variance while achieving a desirable mean response. This article investigates fractional factorial designs...
The Bell regression model (BRM) is a statistical model that is often used in the analysis of count data that exhibits overdispersion. In this study, we propose a Bayesian analysis of the BRM and offer a new perspective on its application. Specificall...
In this article, we establish a constructive nonparametric scheme for monitoring the quality of services provided by a transportation company. The proposed methodology aims at achieving the diligent tracking of the underlying process and the swift de...
In Bayesian statistics, the prior distributions play a key role in the inference, and there are procedures for finding prior distributions. An important problem is that these procedures often lead to improper prior distributions that cannot be normal...
The durations of wars fought between 1480 and 1941 A.D. were found to be well represented by random numbers chosen from a single-event Poisson distribution with a half-life of (1.25 ± 0.1) years. This result complements the work of L.F. Richar...
Benford’s law states that in many real-world datasets, the probability that the leading digit is d equals
Count data are present in many areas of everyday life. Unfortunately, such data are often characterized by over- and under-dispersion. In 1986, Efron introduced the Double Poisson distribution to account for this problem. The aim of this work is to e...
The theoretical modeling of a dynamic system will have derivatives of the response (y) with respect to time (t). Two common physical attributes (i.e., parameters) of dynamic systems are dead-time (θ) and lag (τ). Theoretical dynamic modelin...
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In this article, we propose a goodness-of-fit test for a one-parameter Lindley distribution based on energy statistics. The Lindley distribution has been widely used in reliability studies and survival analysis, especially in applied sciences. The pr...
This paper investigates how diversity among training samples impacts the predictive performance of a subsampling-based ensemble. It is well known that diverse training samples improve ensemble predictions, and smaller subsampling rates naturally lead...
This study provides a systematic overview of 207 econometrics-related R packages identified through CRAN and the Econometrics Task View. Using descriptive and inferential statistics and text mining to compute the word frequency and association among...
Traditional regression estimators like Ordinary Least Squares (OLS) and classical ridge regression often fail under multicollinearity and outlier contamination respectively. Although recently developed two-parameter ridge regression (TPRR) estimators...
