We describe a regression model for multivariate responses of mixed type, where association between outcomes is modeled through dependent, outcome- specific, latent effects. Parametric specifications of this model already exist in the literature; in this paper, we show how model parameters can be estimated in a Finite Mixture (FM) context. While ML estimation, once identifiability has been estab- lished, is quite straightforward, a relevant question arises when dependence should be tested vs independence. We review the performance of LRT and penalized like- lihood criteria to assess the presence of dependence between outcome-specific ran- dom effects. The model behavior is investigated through the analysis of simulated datasets.

Regression models for multivariate mixed responses

Nieddu L;
2009-01-01

Abstract

We describe a regression model for multivariate responses of mixed type, where association between outcomes is modeled through dependent, outcome- specific, latent effects. Parametric specifications of this model already exist in the literature; in this paper, we show how model parameters can be estimated in a Finite Mixture (FM) context. While ML estimation, once identifiability has been estab- lished, is quite straightforward, a relevant question arises when dependence should be tested vs independence. We review the performance of LRT and penalized like- lihood criteria to assess the presence of dependence between outcome-specific ran- dom effects. The model behavior is investigated through the analysis of simulated datasets.
2009
9788861294066
Mixed Responses
Dependence
Mixed effect models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14090/1014
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