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Nonparametric Bayes modeling with sample survey weights.

T Kunihama1, A H Herring2, C T Halpern3

  • 1Department of Statistics, University of Washington, Seattle, WA 98195, U.S.A.

Statistics & Probability Letters
|August 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a straightforward Bayesian method for analyzing complex survey data, incorporating sample survey weights effectively. The approach uses adjusted mixture weights to account for stratified sampling designs, improving analysis accuracy.

Keywords:
Biased samplingDirichlet processMixture modelStratified samplingSurvey data

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Area of Science:

  • Statistics
  • Bayesian Inference
  • Survey Methodology

Background:

  • Population studies commonly use stratified sampling designs with varying inclusion probabilities.
  • Existing Bayesian methods for survey data are often complex or fail to account for stratified designs.

Purpose of the Study:

  • To propose a simple Bayesian approach for analyzing stratified survey data.
  • To effectively incorporate sample survey weights into Bayesian analyses.

Main Methods:

  • Modeling the sample distribution as a mixture with adjusted weights.
  • Accounting for uncertainty in weight adjustments.
  • Utilizing Dirichlet process mixtures for simplicity.
  • Developing a Markov chain Monte Carlo algorithm for computation.

Main Results:

  • The proposed method offers a simpler alternative to existing complex models.
  • It effectively accounts for stratified sampling designs and survey weights.
  • Simulations and an application demonstrate the approach's viability.

Conclusions:

  • The novel Bayesian approach provides a practical solution for analyzing stratified survey data.
  • This method enhances the accuracy and applicability of Bayesian inference in complex survey settings.