Nonparametric augmented probability weighting with sparsity

TL;DR

This paper introduces a nonparametric imputation method with sparse learning and an augmented probability weighting framework to handle nonresponse efficiently, especially in high-dimensional settings, demonstrated through simulations and real data.

Contribution

It develops a novel kernel-based sparse learning approach combined with augmented weighting for nonresponse, improving efficiency and covariate selection in high-dimensional data.

Findings

The method effectively identifies informative covariates.

It improves estimation efficiency over traditional methods.

Simulation and real data analyses validate its performance.

Abstract

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 inefficiency without consideration of sparsity. In this paper, we propose a nonparametric imputation method with sparse learning by employing an efficient kernel-based learning gradient algorithm to identify truly informative covariates. Moreover, an augmented probability weighting framework is adopted to improve the estimation efficiency of the nonparametric imputation method and establish the limiting distribution of the corresponding estimator under regularity assumptions. The performance of the proposed method is also supported by several simulated examples and one real-life analysis.