Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion

Jianhua Zhao (), Changchun Shang, Shulan Li, Ling Xin and Philip L. H. Yu
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Jianhua Zhao: Yunnan University of Finance and Economics
Changchun Shang: Yunnan University of Finance and Economics
Shulan Li: Yunnan University of Finance and Economics
Ling Xin: BNU-HKBU United International College
Philip L. H. Yu: The Education University of Hong Kong

Advances in Data Analysis and Classification, 2025, vol. 19, issue 1, No 9, 209-235

Abstract: Abstract 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

Keywords: Factor analysis; BIC; Model selection; Maximum likelihood; Incomplete data; Variational Bayesian; 62D10; 62F07; 62F99; 62H25 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00582-w

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