25: Penalized Maximum Likelihood Estimation in Latent Class Analysis

Music City Center 

Latent Class Analysis (LCA) is a widely used model-based clustering technique for discrete data. However, including irrelevant variables in an LCA model can significantly impact its efficiency and reliability, as seen in many statistical models. Traditional variable selection methods in LCA often rely on stepwise algorithms, which can be computationally intensive and suboptimal. In this study, we reformulate the LCA model as a log-linear model and apply penalized maximum likelihood estimation to achieve simultaneous parameter estimation and variable selection. Through numerical studies, we compare our approach with existing methods and demonstrate its effectiveness using a real dataset.

Latent Class Analysis

Variable selection

Penalized maximum likelihood

Expectation-Maximization Algorithm 

Korean International Statistical Society