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Computational Statistics and Its Applications, 2nd Edition

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Dear Colleagues,

Computational statistics has become an essential basis for modern science in almost every field. Using algorithms and numerical methods, computational statistics may solve a multitude of problems, such as parameter estimation, hypothesis testing, and statistical modelling.

In this Special Issue, we are seeking high-quality research papers in areas of applied and computational statistics. Topics of interest include, but are not limited to, the following:

Probability theory;
Applied statistics;
Bayesian statistics;
Statistical analysis;
Multivariate statistics;
Regression models;
Statistical inference;
Sampling methods;
Statistics algorithms and software;
Digital technologies for statistics.

Prof. Dr. Eva T. López Sanjuán
Prof. Dr. María Isabel Parra Arévalo
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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28 pages, 11942 KiB

Open AccessArticle

Reliability Analysis of Improved Type-II Adaptive Progressively Inverse XLindley Censored Data

by Refah Alotaibi, Mazen Nassar and Ahmed Elshahhat

Axioms 2025, 14(6), 437; https://doi.org/10.3390/axioms14060437 - 2 Jun 2025

Viewed by 148

Abstract

This study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliability and hazard rate features. In the context of reliability, to handle flexible and time-constrained testing frameworks in high-reliability environments, we formulate maximum likelihood estimators versus Bayesian estimates derived via Markov chain Monte Carlo techniques under gamma priors, which effectively capture prior knowledge. Two patterns of asymptotic interval estimates are constructed through the normal approximation of the classical estimates and of the log-transformed classical estimates. On the other hand, from the Markovian chains, two patterns of credible interval estimates are also constructed. A robust simulation study is carried out to compare the classical and Bayesian point estimation methods, along with the four interval estimation methods. This study’s practical usefulness is demonstrated by its analysis of a real-world dataset. The results reveal that both conventional and Bayesian inferential methods function accurately, with the Bayesian outcomes surpassing those of the conventional method. Full article

(This article belongs to the Special Issue Computational Statistics and Its Applications, 2nd Edition)

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20 pages, 2009 KiB

Open AccessArticle

A Novel Robust Test to Compare Covariance Matrices in High-Dimensional Data

by Hasan Bulut

Axioms 2025, 14(6), 427; https://doi.org/10.3390/axioms14060427 - 30 May 2025

Viewed by 190

Abstract

The comparison of covariance matrices is one of the most important assumptions in many multivariate hypothesis tests, such as Hotelling

T^{2}

and MANOVA. The sample covariance matrix, however, is singular in high-dimensional data when the variable number (p) is greater [...] Read more.

The comparison of covariance matrices is one of the most important assumptions in many multivariate hypothesis tests, such as Hotelling

T^{2}

and MANOVA. The sample covariance matrix, however, is singular in high-dimensional data when the variable number (p) is greater than the sample size (n). Therefore, its determinant is zero, and its inverse cannot be calculated. Although many studies addressing this problem are discussed in the Introduction Section, they have not focused on outliers in datasets. In this study, we propose a test statistic that can be used on high-dimensional datasets without being affected by outliers. There is no distributional assumption because our proposed test is permutational. We investigate the performance of the proposed test based on simulation studies and real example data. In all cases, our proposed test demonstrates good type-1 error control, power, and robustness. Additionally, we have constructed an R function and added it to the “MVTests” package. Therefore, our proposed test can be performed easily on real datasets. Full article

(This article belongs to the Special Issue Computational Statistics and Its Applications, 2nd Edition)

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