By modeling the observed outcome with a location-scale model and fitting an induced logistic regression for missingness, the proposed estimator avoids the multiple-root instability of inverse probability weighting for non-ignorable missing data.
Statistical Inference after Kernel Ridge Regression Imputation under item nonresponse
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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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Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
By modeling the observed outcome with a location-scale model and fitting an induced logistic regression for missingness, the proposed estimator avoids the multiple-root instability of inverse probability weighting for non-ignorable missing data.