Abstract:
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Burgette LF,Reiter JPdoi
10.1111/j.1541-0420.2011.01639.xsubject
Has Abstractpub_date
2012-03-01 00:00:00pages
92-100issue
1eissn
0006-341Xissn
1541-0420journal_volume
68pub_type
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