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A note on identifiability conditions in confirmatory factor analysis

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  • Leeb, William

Abstract

Recently, Chen, Li and Zhang established conditions characterizing asymptotic identifiability of latent factors in confirmatory factor analysis. We give an elementary proof showing that a similar characterization holds non-asymptotically, and prove a related result for identifiability of factor loadings.

Suggested Citation

  • Leeb, William, 2021. "A note on identifiability conditions in confirmatory factor analysis," Statistics & Probability Letters, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:stapro:v:178:y:2021:i:c:s0167715221001528
    DOI: 10.1016/j.spl.2021.109190
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    References listed on IDEAS

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    1. Hong, David & Balzano, Laura & Fessler, Jeffrey A., 2018. "Asymptotic performance of PCA for high-dimensional heteroscedastic data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 435-452.
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    4. Yunxiao Chen & Xiaoou Li & Siliang Zhang, 2020. "Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1756-1770, December.
    5. Edgar Dobriban & Art B. Owen, 2019. "Deterministic parallel analysis: an improved method for selecting factors and principal components," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(1), pages 163-183, February.
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