Statistical Research on Missing Data and Applications

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1462

Special Issue Editor


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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

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Published Papers (1 paper)

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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 914
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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