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Determining the number of factors in high-dimensional generalized latent factor models

Chen, Yunxiao ORCID: 0000-0002-7215-2324 and Li, Xiaoou (2021) Determining the number of factors in high-dimensional generalized latent factor models. Biometrika. ISSN 0006-3444

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Identification Number: 10.1093/biomet/asab044

Abstract

As a generalization of the classical linear factor model, generalized latent factor models are useful for analysing multivariate data of different types, including binary choices and counts. This paper proposes an information criterion to determine the number of factors in generalized latent factor models. The consistency of the proposed information criterion is established under a high-dimensional setting, where both the sample size and the number of manifest variables grow to infinity, and data may have many missing values. An error bound is established for the parameter estimates, which plays an important role in establishing the consistency of the proposed information criterion. This error bound improves several existing results and may be of independent theoretical interest. We evaluate the proposed method by a simulation study and an application to Eysenck’s personality questionnaire.

Item Type: Article
Official URL: https://academic.oup.com/biomet
Additional Information: © 2021 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 10 Aug 2021 10:24
Last Modified: 30 Mar 2022 11:54
URI: http://eprints.lse.ac.uk/id/eprint/111574

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