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Nonparametric imputation method for nonresponse in surveys

Author

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  • Caren Hasler

    (University of Neuchâtel
    University of Toronto Scarborough)

  • Radu V. Craiu

    (University of Toronto)

Abstract

Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.

Suggested Citation

  • Caren Hasler & Radu V. Craiu, 2020. "Nonparametric imputation method for nonresponse in surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 25-48, March.
    • Handle: RePEc:spr:stmapp:v:29:y:2020:i:1:d:10.1007_s10260-019-00458-w
    DOI: 10.1007/s10260-019-00458-w
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    References listed on IDEAS

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    1. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    2. Lee, Thomas C. M., 2003. "Smoothing parameter selection for smoothing splines: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 139-148, February.
    3. Qin J. & Leung D. & Shao J., 2002. "Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 193-200, March.
    4. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518, July.
    5. G. Chauvet & J.-C. Deville & D. Haziza, 2011. "On balanced random imputation in surveys," Biometrika, Biometrika Trust, vol. 98(2), pages 459-471.
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