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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.
Journal cover image

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