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

Author

Listed:
  • Jianhua Zhao

    (Yunnan University of Finance and Economics)

  • Changchun Shang

    (Yunnan University of Finance and Economics
    Guilin University of Technology)

  • 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)

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

Suggested Citation

  • Jianhua Zhao & Changchun Shang & Shulan Li & Ling Xin & Philip L. H. Yu, 2025. "Choosing the number of factors in factor analysis with incomplete data via a novel hierarchical Bayesian information criterion," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(1), pages 209-235, March.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:1:d:10.1007_s11634-024-00582-w
    DOI: 10.1007/s11634-024-00582-w
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    References listed on IDEAS

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