Online Program Home
My Program

Abstract Details

Activity Number: 340 - SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #328717 Presentation
Title: Quantile Regression Analysis of Survey Data Under Informative Sampling
Author(s): Daniel Zhao* and Sixia Chen
Companies: OU Health Sciences Center and University of Oklahoma
Keywords: Complex survey; Informative sampling; Nonparametrics; Quantile regression; Weight smoothing
Abstract:

For complex survey data, the parameter estimates in a quantile regression analysis can be obtained by minimizing a weighted objective function with weights being the original design weights. However, when the complex survey sampling design is informative, the aforementioned design-weighted estimator may be inefficient. In this paper, we proposed several weight smoothing estimators for quantile regression analysis of complex survey data collected with an informative sampling design. In developing our new estimators, we incorporated non-parametric methods for modeling the weight functions and adopted pseudo population bootstrap methods for variance estimation. We then conducted a simulation study to compare all newly proposed methods with the original design-based method in terms of bias, standard error, mean squared error, and coverage property. Results from the simulation study showed that our proposed estimators had smaller bias and mean squared error than did the design-based estimator. We further illustrated and compared all estimators by using the 1988 US National Maternal and Infant Health Survey.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program