Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data

TL;DR

This paper develops a regularization-based variable selection method that effectively handles both ignorable and nonignorable missing data mechanisms, with proven theoretical properties and demonstrated through simulations and real data analysis.

Contribution

It introduces a flexible regularization approach for variable selection under complex missing data mechanisms, extending existing methods to more realistic scenarios.

Findings

Method achieves variable selection consistency.

Effective in both low and high dimensional data.

Validated through simulations and real data applications.

Abstract

The regularization approach for variable selection was well developed for a completely observed data set in the past two decades. In the presence of missing values, this approach needs to be tailored to different missing data mechanisms. In this paper, we focus on a flexible and generally applicable missing data mechanism, which contains both ignorable and nonignorable missing data mechanism assumptions. We show how the regularization approach for variable selection can be adapted to the situation under this missing data mechanism. The computational and theoretical properties for variable selection consistency are established. The proposed method is further illustrated by comprehensive simulation studies and real data analyses, for both low and high dimensional settings.