Literature DB >> 31097833

Pseudo-population bootstrap methods for imputed survey data.

S Chen1, D Haziza2, C Léger2, Z Mashreghi3.   

Abstract

The most common way to treat item nonresponse in surveys is to replace a missing value by a plausible value constructed on the basis of fully observed variables. Treating the imputed values as if they were observed may lead to invalid inferences. Bootstrap variance estimators for various finite population parameters are obtained using two pseudo-population bootstrap schemes. We establish the asymptotic properties of the resulting bootstrap variance estimators for population totals and population quantiles. A simulation study suggests that the methods perform well in terms of relative bias and coverage probability.

Keywords:  Bootstrap; Doubly robust estimation; Imputation; Variance estimation

Year:  2019        PMID: 31097833      PMCID: PMC6508281          DOI: 10.1093/biomet/asz001

Source DB:  PubMed          Journal:  Biometrika        ISSN: 0006-3444            Impact factor:   2.445


  4 in total

1.  Doubly robust estimation in missing data and causal inference models.

Authors:  Heejung Bang; James M Robins
Journal:  Biometrics       Date:  2005-12       Impact factor: 2.571

2.  Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.

Authors:  Anastasios A Tsiatis; Marie Davidian
Journal:  Stat Sci       Date:  2007       Impact factor: 2.901

3.  On sampling without replacement with unequal probabilities of selection.

Authors:  M R Sampford
Journal:  Biometrika       Date:  1967-12       Impact factor: 2.445

4.  Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data.

Authors:  Weihua Cao; Anastasios A Tsiatis; Marie Davidian
Journal:  Biometrika       Date:  2009-08-07       Impact factor: 2.445

  4 in total

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