Equivalence Set Restricted Latent Class Models (ESRLCM)
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917686331572224 |
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| author | Bowers, Jesse Culpepper, Steve |
| author_facet | Bowers, Jesse Culpepper, Steve |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equivalence Set Restricted Latent Class Models (ESRLCM) Bowers, Jesse Culpepper, Steve Machine Learning Statistics Theory 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. |
| title | Equivalence Set Restricted Latent Class Models (ESRLCM) |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2406.03653 |