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A Versatile Model for Clustered and Highly Correlated Multivariate Data

Zhang, Yingjuan; Einbeck, Jochen

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Abstract

For the analysis of multivariate data with an approximately one-dimensional latent structure, it is suggested to model this latent variable by a random effect, allowing for the use of mixed model methodology for dimension reduction purposes. We implement this idea through the mixture-based approach for the estimation of random effect models, hence conveniently enabling clustering of observations along the latent linear subspace, and derive the estimators required for the ensuing EM algorithm under several error variance parameterizations. A simulation study is conducted, and several important inferential problems, including clustering, projection, ranking, regression on covariates, and regression of an external response on the predicted latent variable, are considered and illustrated by real data examples.

Citation

Zhang, Y., & Einbeck, J. (2024). A Versatile Model for Clustered and Highly Correlated Multivariate Data. Journal of statistical theory and practice, 18(1), Article 5. https://doi.org/10.1007/s42519-023-00357-0

Journal Article Type Article
Acceptance Date Nov 17, 2023
Online Publication Date Jan 3, 2024
Publication Date Mar 1, 2024
Deposit Date Jan 12, 2024
Publicly Available Date Mar 4, 2024
Journal Journal of Statistical Theory and Practice
Electronic ISSN 1559-8616
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 18
Issue 1
Article Number 5
DOI https://doi.org/10.1007/s42519-023-00357-0
Public URL https://durham-repository.worktribe.com/output/2084999

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http://creativecommons.org/licenses/by/4.0/

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http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.





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