Quantile Regression Analysis of Survey Data Under Informative Sampling. (29th October 2018) - Chen, Sixia; Zhao, Yan Daniel
Abstract: For complex survey data, the parameters in a quantile regression can be estimated by minimizing an objective function with units weighted by the original design weights. However, when the complex survey sampling design is informative (i.e., when the design weights are correlated with the study variable even after conditioning on other covariates), the efficiency of this design-weighted estimator may be improved. In this article, we propose several weight-smoothing estimators for quantile regression analysis of complex survey data collected with an informative sampling design. Our new estimators incorporate nonparametric methods for modeling the weight functions and pseudo-population bootstrap methods for variance estimation. A simulation study compares, our proposed methods with the original design-based method in terms of bias, standard error, mean squared error, and confidence coverage. Our proposed estimators have smaller bias and mean squared error than does the design-based estimator. We further illustrate and compare estimators for the 1988 US National Maternal and Infant Health Survey.