  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.
