Jackknife Variance Estimation for Nearest-Neighbor Imputation

By Jiahua Chen, Jun Shao. 2001

129 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/42592a913a70d6f838c4fd7fbf2847fd219ed4a3

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In Statistics and Probability

Citations in the corpus

A unified approach to linearization variance estimation from survey data after imputation for item nonresponse (2009, methodology): “Moreover, the proposed method, as it stands, does not cover nearest neighbour imputation (Chen & Shao, 2001).”
A Review of Hot Deck Imputation for Survey Non‐response (2010, methodology): “Chen & Shao (2001) show that for nearest neighbour hot deck imputation the adjusted jackknife produces overestimates of the variance since the adjustment term will be zero or near zero, similar to the difficulty in applying the jackknife to the sample median.”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “For classification applications, Steele and Patterson (2000) proposed a weighted nearest neighbors technique based on resampling ideas, Chen and Shao (2001) reported use of jackknife methods to impute missing values for estimation of a design-based mean, and Shao and Sitter (1996) used…”
Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, methodology): “Chen and Shao (2001) proposed a jackknifed variance estimator.”
Learning k for kNN Classification (2017, methodology): “Based on nearest-neighbor imputation, Chen and Shao proposed some jackknife variance estimators, which are asymptotically unbiased and consistent for the example means [Chen and Shao 2001].”