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Quantile Regression Analysis of Survey Data Under Informative Sampling.

Sixia Chen1, Yan Daniel Zhao1

  • 1Department of Biostatistics and Epidemiology, University of Oklahoma, Oklahoma City, 801 Northeast 13th Street, Oklahoma City, Oklahoma, USA.

Journal of Survey Statistics and Methodology
|May 18, 2019
PubMed
Summary
This summary is machine-generated.

New weight-smoothing estimators improve quantile regression for complex survey data with informative sampling designs. These methods reduce bias and mean squared error compared to traditional design-based approaches.

Keywords:
Complex surveyInformative samplingNonparametricQuantile regressionWeight-smoothing

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Area of Science:

  • Statistics
  • Survey Methodology
  • Econometrics

Background:

  • Quantile regression is used for complex survey data, typically by minimizing an objective function weighted by design weights.
  • Informative sampling designs, where weights correlate with study variables, can reduce the efficiency of standard estimators.

Purpose of the Study:

  • To propose novel weight-smoothing estimators for quantile regression analysis of complex survey data with informative sampling designs.
  • To enhance the efficiency and reduce bias in parameter estimation for such data.

Main Methods:

  • Development of nonparametric methods for modeling weight functions.
  • Incorporation of pseudo-population bootstrap methods for robust variance estimation.
  • Comparison of proposed estimators against the traditional design-based method via simulation.

Main Results:

  • The proposed weight-smoothing estimators demonstrated reduced bias and mean squared error compared to the design-based estimator.
  • Simulation studies indicated improved efficiency and reliable confidence coverage for the new methods.
  • The methods were illustrated using data from the 1988 US National Maternal and Infant Health Survey.

Conclusions:

  • Weight-smoothing estimators offer a more efficient and accurate approach for quantile regression with informative complex survey data.
  • The proposed methods provide a valuable alternative for analyzing complex survey data, particularly when informative sampling is a concern.
  • Further application and validation of these methods are recommended for public health and social science research.