2020/04/03 by Emanuele Aliverti, David B. Dunson, Aliverti, Emanuele +1
Computer Science · Mathematics · Psychology · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Mental Health Research Topics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2004.01462
openalex publication_date 2020/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multivariate categorical data are routinely collected in many application areas. As the number of cells in the table grows exponentially with the number of variables, many or even most cells will contain zero observations. This severe sparsity motivates appropriate statistical methodologies that effectively reduce the number of free parameters, with penalized log-linear models and latent structure analysis being popular options. This article proposes a fundamentally new class of methods, which we refer to as Mixture of Log Linear models (mills). Combining latent class analysis and log-linear models, mills defines a novel Bayesian methodology to model complex multivariate categorical with flexibility and interpretability. Mills is shown to have key advantages over alternative methods for contingency tables in simulations and an application investigating the relation among suicide attempts and empathy.