Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion
Jianhua Zhao, Changchun Shang, Shulan Li, Ling Xin, Philip L. H. Yu · arXiv (Cornell University) · 2022
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