Burgette Lane F, Reiter Jerome P
Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA.
Biometrics. 2012 Mar;68(1):92-100. doi: 10.1111/j.1541-0420.2011.01639.x. Epub 2011 Jun 20.
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.
我们描述了一种贝叶斯分位数回归模型，该模型对部分设计矩阵采用了验证性因子结构。当协变量是科学确定的潜在因子的指标，且分析师试图将这些潜在因子作为分位数回归中的预测变量时，此模型是适用的。我们将该模型应用于一项出生体重研究，其中代表心理社会健康和实际烟草使用情况的潜在变量对响应分布较低分位数的影响是我们感兴趣的。这些模型可以使用一个名为factorQR的R包来拟合。
