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Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data
With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible...
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| 出版年: | J Educ Behav Stat |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
SAGE Publications
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6187066/ https://ncbi.nlm.nih.gov/pubmed/30369783 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3102/1076998618769871 |
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