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Mathematics
Special Issues
Statistical Research on Missing Data and Applications
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Statistical Research on Missing Data and Applications

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2075

Special Issue Editor

Dr. Soeun Kim

E-Mail Website
Guest Editor
Department of Mathematics, Physics, and Statistics, Azusa Pacific University, Azusa, CA, USA
Interests: statistical methods for incomplete data and applications in biostatistics, business analytics, education, psychology and clinical research

Special Issue Information

Dear Colleagues,

This Special Issue is focused on the topic of statistical research on missing data and applications. Papers related to the theoretical or methodological aspects of statistical methods for dealing with missing data, as well as papers focused on the application of analyzing data with missing values, are welcome to be submitted to this Special Issue. Analytic recommendations for practitioners in a particular field or for particular types of data are also encouraged.

The topics of interest of this Special Issue include, but are not limited to, the following:

  • Imputation-based methods for dealing with missing data;
  • Weight-based methods for dealing with missing data;
  • Missing data mechanisms;
  • Analyses under missing not at random (MNAR) assumptions;
  • Longitudinal analysis with missing data;
  • High-dimensional data with missing values;
  • Bayesian inference;
  • Causal inference.

Dr. Soeun Kim
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.

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.

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Published Papers (2 papers)

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28 pages, 488 KiB  
Article
Exploring a Diagnostic Test for Missingness at Random
by Dominick Sutton, Anahid Basiri and Ziqi Li
Mathematics 2025, 13(11), 1728; https://doi.org/10.3390/math13111728 - 23 May 2025
Abstract
Missing data remain a challenge for researchers and decision-makers due to their impact on analytical accuracy and uncertainty estimation. Many studies on missing data are based on randomness, but randomness itself is problematic. This makes it difficult to identify missing data mechanisms and [...] Read more.
Missing data remain a challenge for researchers and decision-makers due to their impact on analytical accuracy and uncertainty estimation. Many studies on missing data are based on randomness, but randomness itself is problematic. This makes it difficult to identify missing data mechanisms and affects how effectively the missing data impacts can be minimized. The purpose of this paper is to examine a potentially simple test to diagnose whether the missing data are missing at random. Such a test is developed using an extended taxonomy of missing data mechanisms. A key aspect of the approach is the use of single mean imputation for handling missing data in the test development dataset. Changing this to random imputation from the same underlying distribution, however, has a negative impact on the diagnosis. This is aggravated by the possibility of high inter-variable correlation, confounding, and mixed missing data mechanisms. The verification step uses data from a high-quality real-world dataset and finds some evidence—in one case—that the data may be missing at random, but this is less persuasive in the second case. Confidence in these results, however, is limited by the potential influence of correlation, confounding, and mixed missingness. This paper concludes with a discussion of the test’s merits and finds that sufficient uncertainties remain to render it unreliable, even if the initial results appear promising. Full article
(This article belongs to the Special Issue Statistical Research on Missing Data and Applications)
15 pages, 476 KiB  
Article
Gaussian Graphical Model Estimation and Selection for High-Dimensional Incomplete Data Using Multiple Imputation and Horseshoe Estimators
by Yunxi Zhang and Soeun Kim
Mathematics 2024, 12(12), 1837; https://doi.org/10.3390/math12121837 - 13 Jun 2024
Viewed by 1255
Abstract
Gaussian graphical models have been widely used to measure the association networks for high-dimensional data; however, most existing methods assume fully observed data. In practice, missing values are inevitable in high-dimensional data and should be handled carefully. Under the Bayesian framework, we propose [...] Read more.
Gaussian graphical models have been widely used to measure the association networks for high-dimensional data; however, most existing methods assume fully observed data. In practice, missing values are inevitable in high-dimensional data and should be handled carefully. Under the Bayesian framework, we propose a regression-based approach to estimating sparse precision matrix for high-dimensional incomplete data. The proposed approach nests multiple imputation and precision matrix estimation with horseshoe estimators in a combined Gibbs sampling process. For fast and efficient selection using horseshoe priors, a post-iteration 2-means clustering strategy is employed. Through extensive simulations, we show the predominant selection and estimation performance of our approach compared to several prevalent methods. We further demonstrate the proposed approach to incomplete genetics data compared to alternative methods applied to completed data. Full article
(This article belongs to the Special Issue Statistical Research on Missing Data and Applications)
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