Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion
| Autor: | Zhao, Jianhua, Shang, Changchun, Li, Shulan, Xin, Ling, Yu, Philip L. H. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size $N$, is a popular model selection criterion for factor analysis with complete data. This definition has also been suggested for incomplete data. However, the penalty term based on the `complete' sample size $N$ is the same no matter whether in a complete or incomplete data case. For incomplete data, there are often only $N_i |
| Databáze: | arXiv |
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