A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation

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.