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 1133

Special Issue Editor


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.

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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 712
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)
Show Figures

Figure 1

Back to TopTop