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Mathematics
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Computational Statistics and Data Analysis, 2nd Edition
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Computational Statistics and Data Analysis, 2nd Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 8967

Special Issue Editor

Prof. Dr. Gaorong Li

E-Mail Website
Guest Editor
School of Statistics, Beijing Normal University, Beijing 100875, China
Interests: high-dimensional statistics; nonparametric statistics and complex data analysis; model/variable selection; statistical learning; causal inference; longitudinal/panel data analysis; measurement error model; empirical likelihood
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the development of scientific techniques, computational statistics and data analysis have become more and more important in diverse areas of science, engineering, and humanities, ranging from genomics and health sciences to economics, finance, and machine learning. To analyze the real data in these fields, statistical methodologies and computing for data analysis are fundamental to statistical modeling and data analysis. In this Special Issue, we are looking for high-quality research papers in computational statistics and data analysis. We invite investigators to contribute original research articles as well as review articles that will stimulate the development of statistical methodology and applications concerning the data analysis.

Prof. Dr. Gaorong Li
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. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • bootstrapping
  • classification
  • data analytical strategies and methodologies applied in biostatistics
  • dimension reduction of high-dimensional data analysis
  • large-scale inference for Gaussian graphical models and covariance estimation
  • longitudinal/panel data analysis
  • massive networks
  • medical statistics
  • nonparametric and semiparametric models
  • optimal portfolio
  • robust statistics
  • statistical methodology and computing for data analysis
  • statistical methodology and computing for noise data, such as measurement error data, missing data etc.
  • sufficient dimension reduction methods in regression analysis variable/model selection for high-dimensional data

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Published Papers (6 papers)

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Research

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19 pages, 354 KiB  
Article
Identifiability and Estimation for Potential-Outcome Means with Misclassified Outcomes
by Shaojie Wei, Chao Zhang, Zhi Geng and Shanshan Luo
Mathematics 2024, 12(18), 2801; https://doi.org/10.3390/math12182801 - 10 Sep 2024
Viewed by 803
Abstract
Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit rate, among others, can also be identified. [...] Read more.
Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit rate, among others, can also be identified. However, current identification and estimation methods for these means often implicitly assume that the collected data for analysis are measured precisely. In many fields such as medicine and economics, the collected variables may be subject to measurement errors, such as medical diagnostic results and individual wage data. Misclassification, as a non-classic measurement error, can lead to severely biased estimates in causal inference. In this paper, we leverage a combined sample to study the identifiability of potential-outcome means corresponding to different treatment levers under a plausible misclassification assumption for the outcome, allowing the misclassification probability to depend on not only the true outcome but also the covariates. Furthermore, we propose the multiply-robust and semiparametric efficient estimators for the means, consistent even under partial misspecification of the observed data law, based on the semiparametric theory framework. The simulation studies and real data analysis demonstrate the satisfactory performance of the proposed method. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
16 pages, 3039 KiB  
Article
Testing Multivariate Normality Based on Beta-Representative Points
by Yiwen Cao, Jiajuan Liang, Longhao Xu and Jiangrui Kang
Mathematics 2024, 12(11), 1711; https://doi.org/10.3390/math12111711 - 30 May 2024
Viewed by 503
Abstract
Testing multivariate normality in high-dimensional data analysis has been a long-lasting topic in the area of goodness of fit. Numerous methods for this purpose can be found in the literature. Reviews on different methods given by influential researchers show that new methods keep [...] Read more.
Testing multivariate normality in high-dimensional data analysis has been a long-lasting topic in the area of goodness of fit. Numerous methods for this purpose can be found in the literature. Reviews on different methods given by influential researchers show that new methods keep emerging in the literature from different perspectives. The theory of statistical representative points provides a new perspective to construct tests for multivariate normality. To avoid the difficulty and huge computational load in finding the statistical representative points from a high-dimensional probability distribution, we develop an approach to constructing a test for high-dimensional normal distribution based on the representative points of the simple univariate beta distribution. The representative-points-based approach is extended to the the case that the sample size may be smaller than the dimension. A Monte Carlo study shows that the new test is able to control type I error rates fairly well for both large and small sample sizes when faced with a high dimension. The power of the new test against some non-normal distributions is generally or substantially improved for a set of selected alternative distributions. A real-data example is given for a simple application illustration. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
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20 pages, 549 KiB  
Article
Estimation in Semi-Varying Coefficient Heteroscedastic Instrumental Variable Models with Missing Responses
by Weiwei Zhang, Jingxuan Luo and Shengyun Ma
Mathematics 2023, 11(23), 4853; https://doi.org/10.3390/math11234853 - 2 Dec 2023
Viewed by 1099
Abstract
This paper studies the estimation problem for semi-varying coefficient heteroscedastic instrumental variable models with missing responses. First, we propose the adjusted estimators for unknown parameters and smooth functional coefficients utilizing the ordinary profile least square method and instrumental variable adjustment technique with complete [...] Read more.
This paper studies the estimation problem for semi-varying coefficient heteroscedastic instrumental variable models with missing responses. First, we propose the adjusted estimators for unknown parameters and smooth functional coefficients utilizing the ordinary profile least square method and instrumental variable adjustment technique with complete data. Second, we present an adjusted estimator of the stochastic error variance by employing the Nadaraya–Watson kernel estimation technique. Third, we apply the inverse probability-weighted method and instrumental variable adjustment technique to construct the adaptive-weighted adjusted estimators for unknown parameters and smooth functional coefficients. The asymptotic properties of our proposed estimators are established under some regularity conditions. Finally, numerous simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed estimators. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
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13 pages, 318 KiB  
Article
Regression Analysis of Dependent Current Status Data with Left Truncation
by Mengyue Zhang, Shishun Zhao, Tao Hu, Da Xu and Jianguo Sun
Mathematics 2023, 11(16), 3539; https://doi.org/10.3390/math11163539 - 16 Aug 2023
Viewed by 1070
Abstract
Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In [...] Read more.
Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
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16 pages, 325 KiB  
Article
An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations
by Wenqing Jiang, Jiangjie Zhou and Baosheng Liang
Mathematics 2023, 11(14), 3233; https://doi.org/10.3390/math11143233 - 22 Jul 2023
Viewed by 2183
Abstract
Dunnett’s procedure has been frequently used for multiple comparisons of group means of several treatments with a control, in drug development and other areas. However, in practice, researchers usually face missing observations when performing Dunnett’s procedure. This paper presents an improved Dunnett’s procedure [...] Read more.
Dunnett’s procedure has been frequently used for multiple comparisons of group means of several treatments with a control, in drug development and other areas. However, in practice, researchers usually face missing observations when performing Dunnett’s procedure. This paper presents an improved Dunnett’s procedure that can construct unique ensemble confidence intervals for comparing group means of several treatments with a control, in the presence of missing observations, using a derived multivariate t distribution under the framework of Rubin’s rule. This procedure fills the current research gap that Rubin’s repeated-imputation inferences cannot adjust for multiplicity and, thereby, cannot give a unified confidence interval to control the family-wise error rate (FWER) when dealing with this problem. Simulation results show that the constructed pooled confidence intervals archive nominal joint coverage and the interval estimations preserve comparable precision to Rubin’s repeated-imputation inference as the missing rate increases. The proposed procedure with propensity-score imputation method is shown to produce more accurate interval estimations and control the FWER well. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
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Review

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22 pages, 428 KiB  
Review
Overview of High-Dimensional Measurement Error Regression Models
by Jingxuan Luo, Lili Yue and Gaorong Li
Mathematics 2023, 11(14), 3202; https://doi.org/10.3390/math11143202 - 21 Jul 2023
Cited by 1 | Viewed by 1715
Abstract
High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained momentum due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size [...] Read more.
High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained momentum due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of the objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several novel statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring in future research. Full article
(This article belongs to the Special Issue Computational Statistics and Data Analysis, 2nd Edition)
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