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Nearest neighbour imputation under single index models

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  • Jun Shao
  • Lei Wang

Abstract

A popular imputation method used to compensate for item nonresponse in sample surveys is the nearest neighbour imputation (NNI) method utilising a covariate to defined neighbours. When the covariate is multivariate, however, NNI suffers the well-known curse of dimensionality and gives unstable results. As a remedy, we propose a single-index NNI when the conditional mean of response given covariates follows a single index model. For estimating the population mean or quantiles, we establish the consistency and asymptotic normality of the single-index NNI estimators. Some limited simulation results are presented to examine the finite-sample performance of the proposed estimator of population mean.

Suggested Citation

  • Jun Shao & Lei Wang, 2019. "Nearest neighbour imputation under single index models," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(2), pages 208-212, July.
    • Handle: RePEc:taf:tstfxx:v:3:y:2019:i:2:p:208-212
    DOI: 10.1080/24754269.2019.1675409
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