Equivalence Set Restricted Latent Class Models (ESRLCM)
Machine Learning
2024-06-07 v1 Machine Learning
Statistics Theory
Statistics Theory
Abstract
Latent Class Models (LCMs) are used to cluster multivariate categorical data, commonly used to interpret survey responses. We propose a novel Bayesian model called the Equivalence Set Restricted Latent Class Model (ESRLCM). This model identifies clusters who have common item response probabilities, and does so more generically than traditional restricted latent attribute models. We verify the identifiability of ESRLCMs, and demonstrate the effectiveness in both simulations and real-world applications.
Cite
@article{arxiv.2406.03653,
title = {Equivalence Set Restricted Latent Class Models (ESRLCM)},
author = {Jesse Bowers and Steve Culpepper},
journal= {arXiv preprint arXiv:2406.03653},
year = {2024}
}
Comments
43 pages, 10 tables, 1 figure