Review Articles

Nearest neighbour imputation under single index models

Jun Shao ,

School of Statistics, East China Normal University, Shanghai, People's Republic of China; Department of Statistics, University of Wisconsin, Madison, WI, USA

Lei Wang

School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China

Pages 208-212 | Received 22 Jul. 2021, Accepted 22 Jul. 2021, Published online: 22 Jul. 2021,
  • Abstract
  • Full Article
  • References
  • Citations


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.


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