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A unified approach to linearization variance estimation from survey data after imputation for item nonresponse

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  • Jae Kwang Kim
  • J. N. K. Rao

Abstract

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple imputation and composite imputation. Results from a simulation study, under a factorial structure for the sampling, response and imputation mechanisms, show that the proposed linearization variance estimator performs well in terms of relative bias, assuming a missing at random response mechanism. Copyright 2009, Oxford University Press.

Suggested Citation

  • Jae Kwang Kim & J. N. K. Rao, 2009. "A unified approach to linearization variance estimation from survey data after imputation for item nonresponse," Biometrika, Biometrika Trust, vol. 96(4), pages 917-932.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:4:p:917-932
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    File URL: http://hdl.handle.net/10.1093/biomet/asp041
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    Citations

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    Cited by:

    1. Jae‐Kwang Kim & Siu‐Ming Tam, 2021. "Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference," International Statistical Review, International Statistical Institute, vol. 89(2), pages 382-401, August.
    2. Sixia Chen & David Haziza, 2017. "Multiply robust imputation procedures for zero-inflated distributions in surveys," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 333-343, December.
    3. Frank Potter & Eric Grau & John Czajka & Dan Scheer & Mark Levitan, "undated". "Imputation Variance Estimation Protocols for the NAS Poverty Measure: The New York City Poverty Measure Experience," Mathematica Policy Research Reports 77be49e0f91f41e888de5139e, Mathematica Policy Research.
    4. Chauvet, Guillaume & Do Paco, Wilfried, 2018. "Exact balanced random imputation for sample survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 1-16.
    5. repec:mpr:mprres:6788 is not listed on IDEAS
    6. Damião N. Da Silva & Li‐Chun Zhang, 2021. "A calibrated imputation method for secondary data analysis of survey data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 25-41, March.
    7. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    8. Lili Yu & Yichuan Zhao, 2022. "A Bootstrap Method for a Multiple-Imputation Variance Estimator in Survey Sampling," Stats, MDPI, vol. 5(4), pages 1-11, November.

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