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Estimation for volunteer web survey samples using a model-averaging approach.

Zhan Liu1, Junbo Zheng1, Chaofeng Tu1

  • 1Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.

Journal of Applied Statistics
|November 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model-averaging approach for more accurate population estimates from web surveys. Combining logistic regression and boosted models improves propensity score estimation for volunteer samples.

Keywords:
Volunteer web survey samplegeneralized boosted modellogistic regression modelmodel-averaging approachpropensity score

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Area of Science:

  • Statistics
  • Survey Methodology
  • Computational Statistics

Background:

  • Volunteer web survey samples often require statistical adjustments for accurate population estimation.
  • Propensity score methods are widely used but sensitive to model choice.
  • Existing methods yield varying population estimates due to different propensity score models.

Purpose of the Study:

  • To develop a more accurate population estimation method for volunteer web survey samples.
  • To improve propensity score estimation by combining parametric and nonparametric models.
  • To establish the theoretical properties and practical implementation of the proposed method.

Main Methods:

  • Proposed a model-averaging estimation approach for propensity scores.
  • Utilized estimates from a parametric logistic regression model and a nonparametric generalized boosted model.
  • Established consistency and asymptotic normality of the proposed estimators.
  • Developed a computational algorithm for implementation.

Main Results:

  • The proposed model-averaging approach demonstrated improved accuracy in simulation studies.
  • The method provides a robust way to combine different propensity score models.
  • Theoretical properties (consistency, asymptotic normality) of the estimators were established.

Conclusions:

  • Model-averaging propensity score estimation offers a more reliable method for volunteer web surveys.
  • The proposed approach enhances the accuracy of population estimates compared to single-model methods.
  • The methodology is validated through simulations and a real-world survey dataset (NSAS).