Random effect models for multivariate mixed data: A Parafac-based finite mixture approach. (February 2022)

• Record Type: Journal Article
• Authors: Alfò, Marco; Giordani, Paolo
• Other Names: Francis Brian guest-editor.; Hinde John guest-editor.
• Is Part Of: Statistical modelling. Volume 22:Number 1/2(2022)
• Page Start: 46
• Page End: 66
• Publication Date: 2022-02
• Subjects: multivariate mixed responses -- Finite mixtures -- random effects -- tensor analysis -- Parafac; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X211037405
• Languages: English
• ISSNs: 1471-082X

Abstract

We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can be further simplified by using the Parafac model. A case study illustrates the proposal.