Dependent Latent Class Models
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Abstract (arXiv)
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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References · 4
- 2013Understanding predictive information criteria for Bayesian modelscited by 51
- 2021Bayesian Pyramids: Identifiable Multilayer Discrete Latent Structure Models for Discrete Datacited by 16
- 2022Blessing of Dependence: Identifiability and Geometry of Discrete Models with Multiple Binary Latent Variablescited by 5
- 2014Finite mixture model of conditional dependencies modes to cluster categorical datacited by 1
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