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Medientyp:
E-Artikel
Titel:
A Note on Ising Network Analysis with Missing Data
Beteiligte:
Zhang, Siliang;
Chen, Yunxiao
Erschienen:
Springer Science and Business Media LLC, 2024
Erschienen in:
Psychometrika (2024)
Sprache:
Englisch
DOI:
10.1007/s11336-024-09985-2
ISSN:
0033-3123;
1860-0980
Entstehung:
Anmerkungen:
Beschreibung:
AbstractThe 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).