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Mathematics
Special Issues
Methods and Applications in Multivariate Statistics
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Methods and Applications in Multivariate Statistics

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2042

Special Issue Editors

Prof. Dr. Xu Guo

E-Mail Website
Guest Editor
School of Statistics, Beijing Normal University, Beijing 100875, China
Interests: hypotheses testing; high-dimensional data analysis; semiparametric regression models; causal inference
Dr. Xuehu Zhu

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
Interests: high-dimensional data analysis; statistical learning; change-point detection​​​​​​​​; applied statistics

Special Issue Information

Dear Colleagues,

Multivariate statistics which deal with multidimensional data play critical role in many scientific areas, with applications ranging from economics, finance, and psychology to public health, epidemiology, and genomics. The theories and methods of multivariate statistics have developed quickly. However, with the development of scientific techniques, the dimensions of the data are very large, growing far larger than even larger the previously than the sample size. New theories and methods of multivariate statistics are required to answer new questions in modern data analysis.

In this Special Issue, we are seeking to publish high-quality research papers on the subject of Methods and Applications in Multivariate Statistics. We invite scholars to contribute original research articles as well as review articles that will stimulate the continuing efforts to develop statistical theory, methodology and applications concerning multivariate statistics.

Prof. Dr. Xu Guo
Dr. Xuehu Zhu
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

  • hypothesis testing
  • classification
  • dimension reduction of multidimensional data analysis
  • variable/model selection for high-dimensional data
  • statistical inference
  • longitudinal/panel data analysis
  • nonparametric and semiparametric models
  • robust statistics

Published Papers (2 papers)

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Research

13 pages, 1503 KiB  
Article
Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm
by Fanqun Li, Shanran Wei and Mingtao Zhao
Mathematics 2024, 12(1), 18; https://doi.org/10.3390/math12010018 (registering DOI) - 21 Dec 2023
Viewed by 648
Abstract
In this paper, based on the mixed Gibbs sampling algorithm, a Bayesian estimation procedure is proposed for a new Pareto-type distribution in the case of complete and type II censored samples. Simulation studies show that the proposed method is consistently superior to the [...] Read more.
In this paper, based on the mixed Gibbs sampling algorithm, a Bayesian estimation procedure is proposed for a new Pareto-type distribution in the case of complete and type II censored samples. Simulation studies show that the proposed method is consistently superior to the maximize likelihood estimation in the context of small samples. Also, an analysis of some real data is provided to test the Bayesian estimation. Full article
(This article belongs to the Special Issue Methods and Applications in Multivariate Statistics)
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21 pages, 463 KiB  
Article
Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data
by Shuaishuai Chen and Jun Lu
Mathematics 2023, 11(10), 2398; https://doi.org/10.3390/math11102398 - 22 May 2023
Viewed by 936
Abstract
Ultrahigh-dimensional grouped data are frequently encountered by biostatisticians working on multi-class categorical problems. To rapidly screen out the null predictors, this paper proposes a quantile-composited feature screening procedure. The new method first transforms the continuous predictor to a Bernoulli variable, by thresholding the [...] Read more.
Ultrahigh-dimensional grouped data are frequently encountered by biostatisticians working on multi-class categorical problems. To rapidly screen out the null predictors, this paper proposes a quantile-composited feature screening procedure. The new method first transforms the continuous predictor to a Bernoulli variable, by thresholding the predictor at a certain quantile. Consequently, the independence between the response and each predictor is easy to judge, by employing the Pearson chi-square statistic. The newly proposed method has the following salient features: (1) it is robust against high-dimensional heterogeneous data; (2) it is model-free, without specifying any regression structure between the covariate and outcome variable; (3) it enjoys a low computational cost, with the computational complexity controlled at the sample size level. Under some mild conditions, the new method was shown to achieve the sure screening property without imposing any moment condition on the predictors. Numerical studies and real data analyses further confirmed the effectiveness of the new screening procedure. Full article
(This article belongs to the Special Issue Methods and Applications in Multivariate Statistics)
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