Modeling adverse birth outcomes via confirmatory factor quantile regression.
Publication
, Journal Article
Burgette, LF; Reiter, JP
Published in: Biometrics
March 2012
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.
Duke Scholars
Published In
Biometrics
DOI
EISSN
1541-0420
ISSN
0006-341X
Publication Date
March 2012
Volume
68
Issue
1
Start / End Page
92 / 100
Related Subject Headings
- Tobacco Smoke Pollution
- Statistics & Probability
- Regression Analysis
- Proportional Hazards Models
- Prevalence
- Maternal Exposure
- Infant, Very Low Birth Weight
- Infant, Newborn
- Infant, Low Birth Weight
- Humans
Citation
APA
Chicago
ICMJE
MLA
NLM
Burgette, L. F., & Reiter, J. P. (2012). Modeling adverse birth outcomes via confirmatory factor quantile regression. Biometrics, 68(1), 92–100. https://doi.org/10.1111/j.1541-0420.2011.01639.x
Burgette, Lane F., and Jerome P. Reiter. “Modeling adverse birth outcomes via confirmatory factor quantile regression.” Biometrics 68, no. 1 (March 2012): 92–100. https://doi.org/10.1111/j.1541-0420.2011.01639.x.
Burgette LF, Reiter JP. Modeling adverse birth outcomes via confirmatory factor quantile regression. Biometrics. 2012 Mar;68(1):92–100.
Burgette, Lane F., and Jerome P. Reiter. “Modeling adverse birth outcomes via confirmatory factor quantile regression.” Biometrics, vol. 68, no. 1, Mar. 2012, pp. 92–100. Epmc, doi:10.1111/j.1541-0420.2011.01639.x.
Burgette LF, Reiter JP. Modeling adverse birth outcomes via confirmatory factor quantile regression. Biometrics. 2012 Mar;68(1):92–100.
Published In
Biometrics
DOI
EISSN
1541-0420
ISSN
0006-341X
Publication Date
March 2012
Volume
68
Issue
1
Start / End Page
92 / 100
Related Subject Headings
- Tobacco Smoke Pollution
- Statistics & Probability
- Regression Analysis
- Proportional Hazards Models
- Prevalence
- Maternal Exposure
- Infant, Very Low Birth Weight
- Infant, Newborn
- Infant, Low Birth Weight
- Humans