A two-level multivariate response model for data with latent structures

Zhang, Yingjuan; Einbeck, Jochen; Drikvandi, Reza

Abstract

A novel approach is proposed for analysing multilevel multivariate response data. The approach is based on identifying a one-dimensional latent variable spanning the space of responses, which then induces correlation between upper-level units. The latent variable, which can be thought of as a random effect, is estimated along with the other model parameters using an EM algorithm, which can be seen in the tradition of the 'nonparametric maximum likelihood' estimator for two-level linear (univariate response) models. Simulations and real data examples from different fields are provided to illustrate the proposed methods in the context of regression and clustering applications.

Citation

Zhang, Y., Einbeck, J., & Drikvandi, R. (online). A two-level multivariate response model for data with latent structures. Statistical Modelling, https://doi.org/10.1177/1471082X241313024

• Journal Article Type: Article
• Acceptance Date: Dec 15, 2024
• Online Publication Date: Feb 7, 2025
• Deposit Date: Feb 19, 2025
• Publicly Available Date: Feb 19, 2025
• Journal: Statistical Modelling
• Print ISSN: 1471-082X
• Electronic ISSN: 1477-0342
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1177/1471082X241313024
• Public URL: https://durham-repository.worktribe.com/output/3490429