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Pseudo-population bootstrap methods for imputed survey data.

S Chen1, D Haziza2, C Léger2

  • 1Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th Street, Oklahoma City, Oklahoma 73126-0901, U.S.A.

Biometrika
|May 18, 2019
PubMed
Summary
This summary is machine-generated.

Imputing missing survey data can lead to incorrect results. This study introduces bootstrap variance estimators using pseudo-population schemes to improve accuracy for survey totals and quantiles.

Keywords:
BootstrapDoubly robust estimationImputationVariance estimation

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Item nonresponse is common in surveys.
  • Imputation is a standard method for handling missing data.
  • Treating imputed values as observed can bias results.

Purpose of the Study:

  • To develop valid bootstrap variance estimators for survey data with imputed values.
  • To assess the performance of these estimators for population totals and quantiles.

Main Methods:

  • Utilized two pseudo-population bootstrap schemes.
  • Derived asymptotic properties for the proposed variance estimators.
  • Conducted a simulation study to evaluate performance.

Main Results:

  • The proposed bootstrap variance estimators are asymptotically valid.
  • Simulation results show good performance in terms of relative bias.
  • Coverage probabilities were adequate in the simulation study.

Conclusions:

  • The developed bootstrap methods provide valid variance estimation for imputed survey data.
  • These methods improve the reliability of inferences from surveys with item nonresponse.