Author
Listed:
- Francis K. C. Hui
- Samuel Müller
- A. H. Welsh
Abstract
Multivariate data are commonly analyzed using one of two approaches: a conditional approach based on generalized linear latent variable models (GLLVMs) or some variation thereof, and a marginal approach based on generalized estimating equations (GEEs). With research on mixed models and GEEs having gone down separate paths, there is a common mindset to treat the two approaches as mutually exclusive, with which to use driven by the question of interest. In this article, focusing on multivariate binary responses, we study the connections between the parameters from conditional and marginal models, with the aim of using GEEs for fast variable selection in GLLVMs. This is accomplished through two main contributions. First, we show that GEEs are zero consistent for GLLVMs fitted to multivariate binary data. That is, if the true model is a GLLVM but we misspecify and fit GEEs, then the latter is able to asymptotically differentiate between truly zero versus nonzero coefficients in the former. Building on this result, we propose GEE-assisted variable selection for GLLVMs using score- and Wald-based information criteria to construct a fast forward selection path followed by pruning. We demonstrate GEE-assisted variable selection is selection consistent for the underlying GLLVM, with simulation studies demonstrating its strong finite sample performance and computational efficiency.
Suggested Citation
Francis K. C. Hui & Samuel Müller & A. H. Welsh, 2023.
"GEE-Assisted Variable Selection for Latent Variable Models with Multivariate Binary Data,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1252-1263, April.
Handle:
RePEc:taf:jnlasa:v:118:y:2023:i:542:p:1252-1263
DOI: 10.1080/01621459.2021.1987251
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:118:y:2023:i:542:p:1252-1263. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.