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Axioms
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Methods and Applications of Advanced Statistical Analysis, 2nd Edition
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Methods and Applications of Advanced Statistical Analysis, 2nd Edition

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 26 June 2025 | Viewed by 3568

Special Issue Editor

Dr. Tomas Ruzgas

E-Mail Website
Guest Editor
Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, 44249 Kaunas, Lithuania
Interests: nonparametric statistics; application of functional analysis in statistics; hypothesis testing; multivariate analysis in social, environmental, medical sciences, etc.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to exploring the latest advances in statistical analysis that are innovative in their theoretical, methodological, or applicability approach. Potential topics of interest for this Special Issue include, but are not limited to, survey sampling, nonparametric statistics, functional data analysis, Bayesian analysis, robust statistics, hypothesis testing, univariate and multivariate statistics, regression and analysis of variance, categorical data analysis, classification and clustering, mixed modelling, survival analysis, time series analysis, and their applications.

Dr. Tomas Ruzgas
Guest Editor

Manuscript Submission Information

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • application of statistics
  • causal analysis
  • classification and clustering
  • data analysis
  • linear and nonlinear models
  • mixed modeling
  • nonparametric statistics
  • regression and analysis of variance
  • robust statistics
  • time series

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Related Special Issue

Published Papers (4 papers)

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Research

23 pages, 1299 KiB  
Article
Competing Risks in Accelerated Life Testing: A Study on Step-Stress Models with Tampered Random Variables
by Hanan Haj Ahmad, Ehab M. Almetwally and Dina A. Ramadan
Axioms 2025, 14(1), 32; https://doi.org/10.3390/axioms14010032 - 2 Jan 2025
Cited by 1 | Viewed by 699
Abstract
This study introduces a novel approach to accelerated life test experiments by examining competing risk factors using the Tampered Random Variable (TRV) model. This approach remains extensively unexplored in current research. The methodology is implemented for a simple step-stress life test (SSLT) model [...] Read more.
This study introduces a novel approach to accelerated life test experiments by examining competing risk factors using the Tampered Random Variable (TRV) model. This approach remains extensively unexplored in current research. The methodology is implemented for a simple step-stress life test (SSLT) model and accounts for various causes of failure. The Power Chris–Jerry (PCJ) distribution is utilized to model the lifetimes of units under different stress levels, incorporating unique shape parameters while maintaining a fixed-scale parameter. This study employs the TRV model to integrate constant tampering coefficients for each failure cause within step-stress data analysis. Maximum-likelihood estimates for model parameters and tampering coefficients are derived from SSLT data, and some confidence intervals are presented based on the Type-II censoring scheme. Furthermore, Bayesian estimation is applied to the parameters, supported by appropriate prior distributions. The robustness of the proposed method is validated through comprehensive simulations and real-world applications in different scientific domains. Full article
(This article belongs to the Special Issue Methods and Applications of Advanced Statistical Analysis, 2nd Edition)
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20 pages, 3039 KiB  
Article
Bayesian and Non-Bayesian Inference to Bivariate Alpha Power Burr-XII Distribution with Engineering Application
by Dina A. Ramadan, Mustafa M. Hasaballah, Nada K. Abd-Elwaha, Arwa M. Alshangiti, Mahmoud I. Kamel, Oluwafemi Samson Balogun and Mahmoud M. El-Awady
Axioms 2024, 13(11), 796; https://doi.org/10.3390/axioms13110796 - 17 Nov 2024
Viewed by 706
Abstract
In this research, we present a new distribution, which is the bivariate alpha power Burr-XII distribution, based on the alpha power Burr-XII distribution. We thoroughly examine the key features of our newly developed bivariate model. We introduce a new class of bivariate models, [...] Read more.
In this research, we present a new distribution, which is the bivariate alpha power Burr-XII distribution, based on the alpha power Burr-XII distribution. We thoroughly examine the key features of our newly developed bivariate model. We introduce a new class of bivariate models, which are built with the copula function. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, and product moments were studied. This was accomplished with two datasets of real data that came from two distinct devices. We employed Bayesian, maximum likelihood estimation, and least squares estimation strategies to obtain estimated points and intervals. Additionally, we generated bootstrap confidence intervals and conducted numerical analyses using the Markov chain Monte Carlo method. Lastly, we compared this novel bivariate distribution’s performance to earlier bivariate models, to determine how well it fit the real data. Full article
(This article belongs to the Special Issue Methods and Applications of Advanced Statistical Analysis, 2nd Edition)
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14 pages, 455 KiB  
Article
Skew-Symmetric Generalized Normal and Generalized t Distributions
by Najmeh Nakhaei Rad, Mahdi Salehi, Yaser Mehrali and Ding-Geng Chen
Axioms 2024, 13(11), 782; https://doi.org/10.3390/axioms13110782 - 13 Nov 2024
Viewed by 740
Abstract
In this paper, we introduce the skew-symmetric generalized normal and the skew-symmetric generalized t distributions, which are skewed extensions of symmetric special cases of generalized skew-normal and generalized skew-t distributions, respectively. We derive key distributional properties for these new distributions, including a [...] Read more.
In this paper, we introduce the skew-symmetric generalized normal and the skew-symmetric generalized t distributions, which are skewed extensions of symmetric special cases of generalized skew-normal and generalized skew-t distributions, respectively. We derive key distributional properties for these new distributions, including a recurrence relation and an explicit form for the cumulative distribution function (cdf) of the skew-symmetric generalized t distribution. Numerical examples including a simulation study and a real data analysis are presented to illustrate the practical applicability of these distributions. Full article
(This article belongs to the Special Issue Methods and Applications of Advanced Statistical Analysis, 2nd Edition)
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11 pages, 275 KiB  
Article
Calibration Estimators with Different Types of Distance Measures Under Stratified Sampling in the Presence of Measurement Error
by Sat Gupta and Pidugu Trisandhya
Axioms 2024, 13(11), 737; https://doi.org/10.3390/axioms13110737 - 27 Oct 2024
Viewed by 889
Abstract
The calibration method is used in stratified random sampling in the presence of measurement error to achieve optimum strata weights for better precision. In this study we attempt to analyze the behaviour of calibration estimators with different types of distance measures in the [...] Read more.
The calibration method is used in stratified random sampling in the presence of measurement error to achieve optimum strata weights for better precision. In this study we attempt to analyze the behaviour of calibration estimators with different types of distance measures in the presence of measurement error for the estimation of population mean under stratified random sampling. The proposed estimators are compared with the estimators in the absence of measurement error. A simulation study has been carried out to evaluate the proposed estimators. Full article
(This article belongs to the Special Issue Methods and Applications of Advanced Statistical Analysis, 2nd Edition)
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