Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.3
Journals
Mathematics
Special Issues
Methodology and Application in Computational Statistics and Data Science
share announcement

Methodology and Application in Computational Statistics and Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3003

Special Issue Editors

Dr. Wenbo Wu

E-Mail Website
Guest Editor
Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX, USA
Interests: high-dimensional data modelling and inference; dimension reduction; variable selection; causal inference
Dr. Chenlu Ke

E-Mail Website
Guest Editor
Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, USA
Interests: dimension reduction; variable selection; high-dimensional data analysis; multivariate data analysis

Special Issue Information

Dear Colleagues,

Due to recent advancements in the fields of artificial intelligence and machine learning, massive amounts of data have been collected via various channels under different formats. While the growing availability of information would generally lead to significant scientific progression, it also presents great challenges in adequately analyzing such massive datasets. To this end, the scientific literature over various topics of computational statistics and data science have significantly grown in recent years. The following topics have gained increasing attentions:  dimension reduction, feature screening, variable selection, optimal sampling, multitask learning, transfer learning, and distributed learning, among others. In this Special Issue, entitled “Methodology and Application in Computational Statistics and Data Science”, we invite papers to address both the methodological and computational aspects of dealing with challenges in the analysis of large datasets and new data types, including functional data, big data, network data, and others. We also welcome papers that specifically focus on applying the latest methods to the analysis of challenging datasets with complex structures, such as in the areas of biostatistics, genetics, spatial statistics, and others.

Dr. Wenbo Wu
Dr. Chenlu Ke
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • statistical computing
  • big data
  • high-dimensional statistics
  • dimension reduction
  • feature screening
  • variable selection
  • sampling
  • order Statistics
  • missing data
  • causal inference

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 918 KiB  
Article
Standard Error Estimation in Invariance Alignment
by Alexander Robitzsch
Mathematics 2025, 13(12), 1915; https://doi.org/10.3390/math13121915 - 8 Jun 2025
Viewed by 38
Abstract
The invariance alignment (IA) method enables group comparisons in factor models involving either continuous or discrete items. This article evaluates the performance of the commonly used delta method for standard error estimation against alternative bootstrap confidence interval (CI) approaches for IA using the [...] Read more.
The invariance alignment (IA) method enables group comparisons in factor models involving either continuous or discrete items. This article evaluates the performance of the commonly used delta method for standard error estimation against alternative bootstrap confidence interval (CI) approaches for IA using the L0.5 and L0 loss functions. For IA applied to continuous items, both the delta method and all bootstrap methods yielded acceptable coverage rates. In contrast, for dichotomous items, only bias-corrected bootstrap CIs provided reliable statistical inference in moderate to large sample sizes. In small sample sizes with dichotomous items, none of the individual methods performed consistently well. However, a newly proposed average bootstrap CI approach—based on averaging the lower and upper CI limits from two bootstrap methods—achieved acceptable coverage rates. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
14 pages, 873 KiB  
Article
Experimental Study of an Approximate Method for Calculating Entropy-Optimal Distributions in Randomized Machine Learning Problems
by Alexey Yu. Popkov, Yuri A. Dubnov, Ilya V. Sochenkov and Yuri S. Popkov
Mathematics 2025, 13(11), 1821; https://doi.org/10.3390/math13111821 - 29 May 2025
Viewed by 189
Abstract
This paper is devoted to the experimental study of the integral approximation method in entropy optimization problems arising from the application of the Randomized Machine Learning method. Entropy-optimal probability density functions contain normalizing integrals from multivariate exponential functions; as a result, when computing [...] Read more.
This paper is devoted to the experimental study of the integral approximation method in entropy optimization problems arising from the application of the Randomized Machine Learning method. Entropy-optimal probability density functions contain normalizing integrals from multivariate exponential functions; as a result, when computing these distributions in the process of solving an optimization problem, it is necessary to ensure efficient computation of these integrals. We investigate an approach based on the approximation of integrand functions, which are applied to the solution of several configurations of problems with model and real data with linear static models using a symbolic computation mechanism. Computational studies were carried out under the same conditions, with the same initial data and values of hyperparameters of the used models. They have shown the performance and efficiency of the proposed approach in the Randomized Machine Learning problems based on linear static models. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
►▼ Show Figures

Figure 1

20 pages, 1780 KiB  
Article
A Flexible Truncated (u,v)-Half-Normal Distribution: Properties, Estimation and Applications
by Maher Kachour, Hassan S. Bakouch, Mustapha Muhammad, Badamasi Abba, Lamia Alyami and Sadiah M. A. Aljeddani
Mathematics 2025, 13(11), 1740; https://doi.org/10.3390/math13111740 - 24 May 2025
Viewed by 259
Abstract
This study introduces the truncated (u,v)-half-normal distribution, a novel probability model defined on the bounded interval (u,v), with parameters σ and b. This distribution is designed to model processes with restricted domains, [...] Read more.
This study introduces the truncated (u,v)-half-normal distribution, a novel probability model defined on the bounded interval (u,v), with parameters σ and b. This distribution is designed to model processes with restricted domains, ensuring realistic and analytically tractable outcomes. Some key properties of the proposed model, including its cumulative distribution function, probability density function, survival function, hazard rate, and moments, are derived and analyzed. Parameter estimation of σ and b is achieved through a hybrid approach, combining maximum likelihood estimation (MLE) for σ and a likelihood-free-inspired technique for b. A sensitivity analysis highlighting the dependence of σ on b, and an optimal estimation algorithm is proposed. The proposed model is applied to two real-world data sets, where it demonstrates superior performance over some existing models based on goodness-of-fit criteria, such as the known AIC, BIC, CAIC, KS, AD, and CvM statistics. The results emphasize the model’s flexibility and robustness for practical applications in modeling data with bounded support. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
►▼ Show Figures

Figure 1

18 pages, 1702 KiB  
Article
Population Median Estimation Using Auxiliary Variables: A Simulation Study with Real Data Across Sample Sizes and Parameters
by Umer Daraz, Fatimah A. Almulhim, Mohammed Ahmed Alomair and Abdullah Mohammed Alomair
Mathematics 2025, 13(10), 1660; https://doi.org/10.3390/math13101660 - 19 May 2025
Viewed by 246
Abstract
This paper introduces an enhanced class of ratio estimators, which employ the transformation technique on an auxiliary variable under simple random sampling to estimate the population median. The transformation strategy can reduce both the bias and mean square error, which can help estimators [...] Read more.
This paper introduces an enhanced class of ratio estimators, which employ the transformation technique on an auxiliary variable under simple random sampling to estimate the population median. The transformation strategy can reduce both the bias and mean square error, which can help estimators become more efficient. The bias and mean square error of proposed estimators are investigated up to the first order of approximation. Through simulation studies and the analysis of various data sets, the performance of the proposed estimators is compared to existing methods. The proposed class of estimators improves the precision and efficiency of median estimation, ensuring more accurate and dependable results in various practical scenarios. The findings reveal that the new estimators show superior performance under the given conditions compared to traditional estimators. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
►▼ Show Figures

Figure 1

16 pages, 9657 KiB  
Article
A Self-Normalized Online Monitoring Method Based on the Characteristic Function
by Yang Wang and Baoying Yang
Mathematics 2025, 13(5), 710; https://doi.org/10.3390/math13050710 - 22 Feb 2025
Viewed by 337
Abstract
The goal of nonparametric online monitoring methods is to quickly detect structural changes in the distribution of a data stream. This work is concerned with a nonparametric self-normalized monitoring method based on the difference of empirical characteristic functions. This method introduces an additional [...] Read more.
The goal of nonparametric online monitoring methods is to quickly detect structural changes in the distribution of a data stream. This work is concerned with a nonparametric self-normalized monitoring method based on the difference of empirical characteristic functions. This method introduces an additional self-normalization factor, which enables effective control the Type I error. We theoretically investigate the asymptotic properties of the monitoring method under the null hypothesis as well as the alternative hypothesis. Since the asymptotic distribution under the null hypothesis is quite complicated, we apply the multivariate stationary bootstrap method to estimate the critical value of the sequential test. Numerical simulations and a real-world application demonstrate the usefulness of the proposed method. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
►▼ Show Figures

Figure 1

20 pages, 3270 KiB  
Article
Mathematical Limitations of Gravity Model in Constructing Regional Association Networks: A Case Study
by Qing Qin and Lingxiao Li
Mathematics 2024, 12(20), 3180; https://doi.org/10.3390/math12203180 - 11 Oct 2024
Viewed by 1360
Abstract
This study evaluates the limitations of gravity models in constructing regional association networks, using China’s interprovincial economic connections as a case study. Comparison between a gravity-model-based simulated network and an actual network reveals significant topological differences. The gravity model overestimates the influence of [...] Read more.
This study evaluates the limitations of gravity models in constructing regional association networks, using China’s interprovincial economic connections as a case study. Comparison between a gravity-model-based simulated network and an actual network reveals significant topological differences. The gravity model overestimates the influence of larger, inward-oriented provinces and fails to accurately represent external connections. Attempts to refine the model with additional variables proved ineffective. Further theoretical analysis attributes these deficiencies to measurement bias from the model’s simplified binary perspective and information loss due to dimensional mismatch between pairwise predictions and complex network structures. These findings underscore the need for cautious application of gravity models and the development of more comprehensive analytical frameworks in regional network analysis. Full article
(This article belongs to the Special Issue Methodology and Application in Computational Statistics and Data Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop