We consider maximum likelihood estimation of the Latent Class (LC) model, which is formulated through individual discrete latent variables. We explore tempering techniques to overcome the problem of multimodality of the log-likelihood function. A Tempered Expectation-Maximization (T-EM) algorithm is proposed, which can adequately explore the parameter space and reach the global maximum more frequently than the standard EM algorithm. We assess the performance of the proposed approach by a Monte Carlo simulation study and an application based on data about anxiety and depression in oncological patients.

Brusa, L., Bartolucci, F., Pennoni, F. (2021). A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation. In C. Perna, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of short papers SIS 2021. 50th Scientific Meeting of the Italian Statistical Society (pp. 183-188). Milano : Pearson.

A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation

Brusa, L;Pennoni, F
2021

Abstract

We consider maximum likelihood estimation of the Latent Class (LC) model, which is formulated through individual discrete latent variables. We explore tempering techniques to overcome the problem of multimodality of the log-likelihood function. A Tempered Expectation-Maximization (T-EM) algorithm is proposed, which can adequately explore the parameter space and reach the global maximum more frequently than the standard EM algorithm. We assess the performance of the proposed approach by a Monte Carlo simulation study and an application based on data about anxiety and depression in oncological patients.
Capitolo o saggio
annealing, finite mixture models, latent variables, local maxima
English
Book of short papers SIS 2021. 50th Scientific Meeting of the Italian Statistical Society
Perna, C; Salvati, N; Schirripa Spagnolo, F
giu-2021
2021
9788891927361
1
Pearson
183
188
Brusa, L., Bartolucci, F., Pennoni, F. (2021). A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation. In C. Perna, N. Salvati, F. Schirripa Spagnolo (a cura di), Book of short papers SIS 2021. 50th Scientific Meeting of the Italian Statistical Society (pp. 183-188). Milano : Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/322706
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