Modeling adverse birth outcomes via confirmatory factor quantile regression.
Published
Journal Article
We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predictors in the quantile regression. We apply the model to a study of birth weights in which the effects of latent variables representing psychosocial health and actual tobacco usage on the lower quantiles of the response distribution are of interest. The models can be fit using an R package called factorQR.
Full Text
Duke Authors
Cited Authors
- Burgette, LF; Reiter, JP
Published Date
- March 2012
Published In
Volume / Issue
- 68 / 1
Start / End Page
- 92 - 100
PubMed ID
- 21689080
Pubmed Central ID
- 21689080
Electronic International Standard Serial Number (EISSN)
- 1541-0420
International Standard Serial Number (ISSN)
- 0006-341X
Digital Object Identifier (DOI)
- 10.1111/j.1541-0420.2011.01639.x
Language
- eng