Dependent Latent Class Models

• URL: http://arxiv.org/abs/2205.08677v2
• Date: Thu, 27 Apr 2023 12:44:14 GMT
• Title: Dependent Latent Class Models
• Authors: Jesse Bowers, Steve Culpepper
• Abstract summary: Latent Class Models (LCMs) are used to cluster multivariate categorical data. We develop a novel Bayesian model called a Dependent Latent Class Model (DLCM) We demonstrate the effectiveness of DLCMs in both simulations and real-world applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent Class Models (LCMs) are used to cluster multivariate categorical data (e.g. group participants based on survey responses). Traditional LCMs assume a property called conditional independence. This assumption can be restrictive, leading to model misspecification and overparameterization. To combat this problem, we developed a novel Bayesian model called a Dependent Latent Class Model (DLCM), which permits conditional dependence. We verify identifiability of DLCMs. We also demonstrate the effectiveness of DLCMs in both simulations and real-world applications. Compared to traditional LCMs, DLCMs are effective in applications with time series, overlapping items, and structural zeroes.

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