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.
Random effect models for multivariate mixed data: A Parafac-based finite mixture approach / Alfo, M.; Giordani, P.. - In: STATISTICAL MODELLING. - ISSN 1471-082X. - 22:1-2(2022), pp. 46-66. [10.1177/1471082X211037405]
Random effect models for multivariate mixed data: A Parafac-based finite mixture approach
Alfo M.;Giordani P.
2022
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.| File | Dimensione | Formato | |
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