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.
翻译:测算是处理缺失数据的一种常用技术。 我们考虑使用内核脊回归法进行估算的非参数性方法,并提出一致的差异估计。 拟议的差异估计基于一种线性方法,即采用对密度比率进行估计的酶法。 当在内核脊回归估算法中使用Sobolev空间时,估算估计值的根值一致性得到确定, 从而使我们能够开发拟议的差异估测法。 合成数据实验被提出来证实我们的理论。