Variance estimation after Kernel Ridge Regression Imputation

Date
2020-01-01
Authors
Wang, Hengfang
Kim, Jae Kwang
Kim, Jae Kwang
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Altmetrics
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Statistics
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Statistics
Abstract

Imputation is a popular technique for handling missing data. Variance estimation after imputation is an important practical problem in statistics. In this paper, we consider variance estimation of the imputed mean estimator under the kernel ridge regression imputation. We consider a linearization approach which employs the covariate balancing idea to estimate the inverse of propensity scores. The statistical guarantee of our proposed variance estimation is studied when a Sobolev space is utilized to do the imputation, where n-consistency can be obtained. Synthetic data experiments are presented to confirm our theory.

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This paper was presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML), July 17, 2020. Posted with permission.

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