Nonparametric augmented probability weighting with sparsity

Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in modern statistics, so parametric imputation or propensity score weighting usually leads to estimation inefficiency and introduces a large variability without consideration of sparsity. In this paper, we propose a nonparametric imputation method with sparsity to estimate the finite population mean, where an efficient kernel-based method in the reproducing kernel Hilbet space is employed for estimation and sparse learning. Moreover, an augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions. The performance of the proposed method is also supported by several simulated examples and one real-life analysis.

Automated summary

This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.