Statistical Inference after Kernel Ridge Regression Imputation under item nonresponse

• URL: http://arxiv.org/abs/2102.00058v1
• Date: Fri, 29 Jan 2021 20:46:33 GMT
• Title: Statistical Inference after Kernel Ridge Regression Imputation under item nonresponse
• Authors: Hengfang Wang, Jae-Kwang Kim
• Abstract summary: 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.
• Score: 0.76146285961466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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