A note on Ising network analysis with missing data

Zhang, Siliang ORCID: 0000-0002-2641-4944 and Chen, Yunxiao ORCID: 0000-0002-7215-2324 (2024) A note on Ising network analysis with missing data. Psychometrika. ISSN 0033-3123

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Abstract

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya–Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Item Type:	Article
Official URL:	https://link.springer.com/journal/11336
Additional Information:	© 2024 The Author(s)
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	25 Jun 2024 23:14
Last Modified:	19 Nov 2024 04:42
URI:	http://eprints.lse.ac.uk/id/eprint/123984

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