In latent variable models, problems related to the integration of the likelihood function arise since analytical solutions do not exist. Laplace and Adaptive Gauss-Hermite (AGH) approximations have been discussed as good approximating methods. Their performance relies on the assump- tion of normality of the posterior density of the latent variables, but, in small samples, this is not necessarily assured. Here, we analyze how the shape of the posterior densities varies as function of the model parame- ters, and we investigate its influence on the performance of AGH and of the Laplace approximation.

Bianconcini S., Cagnone S. (2014). The role of posterior densities in latent variable models for ordinal data. COMMUNICATIONS IN STATISTICS. THEORY AND METHODS, 43(4), 681-692 [10.1080/03610926.2013.810266].

The role of posterior densities in latent variable models for ordinal data

BIANCONCINI, SILVIA;CAGNONE, SILVIA
2014

Abstract

In latent variable models, problems related to the integration of the likelihood function arise since analytical solutions do not exist. Laplace and Adaptive Gauss-Hermite (AGH) approximations have been discussed as good approximating methods. Their performance relies on the assump- tion of normality of the posterior density of the latent variables, but, in small samples, this is not necessarily assured. Here, we analyze how the shape of the posterior densities varies as function of the model parame- ters, and we investigate its influence on the performance of AGH and of the Laplace approximation.
2014
Bianconcini S., Cagnone S. (2014). The role of posterior densities in latent variable models for ordinal data. COMMUNICATIONS IN STATISTICS. THEORY AND METHODS, 43(4), 681-692 [10.1080/03610926.2013.810266].
Bianconcini S.; Cagnone S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/141871
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