Latent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In presence of many latent variables and/or random effects, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. In literature, different remedies have been proposed to overcome these problems. Among these, the pairwise likelihood method and, more recently, the dimension-wise quadrature have been shown to produce estimators with desirable properties. We compare the performance of the two methods for a class of dynamic latent variable models for count data.

Comparison between Different Likelihood BasedEstimation Methods in Latent Variable Models for Categorical Data / Silvia Bianconcini; Silvia Cagnone. - ELETTRONICO. - (2021), pp. 151-156. (Intervento presentato al convegno Sis 2021 tenutosi a On-line nel 21-25 giugno 2021).

Comparison between Different Likelihood BasedEstimation Methods in Latent Variable Models for Categorical Data

Silvia Bianconcini;Silvia Cagnone
2021

Abstract

Latent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In presence of many latent variables and/or random effects, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. In literature, different remedies have been proposed to overcome these problems. Among these, the pairwise likelihood method and, more recently, the dimension-wise quadrature have been shown to produce estimators with desirable properties. We compare the performance of the two methods for a class of dynamic latent variable models for count data.
2021
Book of Short Papers SIS 2021
151
156
Comparison between Different Likelihood BasedEstimation Methods in Latent Variable Models for Categorical Data / Silvia Bianconcini; Silvia Cagnone. - ELETTRONICO. - (2021), pp. 151-156. (Intervento presentato al convegno Sis 2021 tenutosi a On-line nel 21-25 giugno 2021).
Silvia Bianconcini; Silvia Cagnone
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/827307
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