Statistical Inference after Kernel Ridge Regression Imputation under item nonresponse
Methodology
2021-02-02 v1 Machine Learning
Abstract
Imputation is a popular technique for handling missing data. We consider a nonparametric approach to imputation using the kernel ridge regression technique and propose consistent variance estimation. The proposed variance estimator is based on a linearization approach which employs the entropy method to estimate the density ratio. The root-n consistency of the imputation estimator is established when a Sobolev space is utilized in the kernel ridge regression imputation, which enables us to develop the proposed variance estimator. Synthetic data experiments are presented to confirm our theory.
Cite
@article{arxiv.2102.00058,
title = {Statistical Inference after Kernel Ridge Regression Imputation under item nonresponse},
author = {Hengfang Wang and Jae-Kwang Kim},
journal= {arXiv preprint arXiv:2102.00058},
year = {2021}
}