A pseudo empirical likelihood approach for stratified samples with nonresponse

TL;DR

This paper introduces a pseudo empirical likelihood method for stratified surveys with nonresponse, improving efficiency by avoiding ad hoc collapsing of small categories and providing consistent variance estimation.

Contribution

It develops a novel pseudo empirical likelihood approach that enhances estimator efficiency in stratified samples with nonresponse, without collapsing small categories.

Findings

The proposed method yields more efficient estimators than traditional approaches.

Bootstrap variance estimation is shown to be consistent.

Simulation results demonstrate improved finite sample performance.

Abstract

Nonresponse is common in surveys. When the response probability of a survey variable $Y$ depends on $Y$ through an observed auxiliary categorical variable $Z$ (i.e., the response probability of $Y$ is conditionally independent of $Y$ given $Z$), a simple method often used in practice is to use $Z$ categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents within each imputation cell. This simple method, however, is inefficient when some $Z$ categories have small sizes and ad hoc methods are often applied to collapse small imputation cells. Assuming a parametric model on the conditional probability of $Z$ given $Y$ and a nonparametric model on the distribution of $Y$, we develop a pseudo empirical likelihood method to provide more efficient survey estimators. Our method avoids any ad hoc collapsing small $Z$ categories, since reweighting or imputation is done across $Z$ categories. Asymptotic distributions for estimators of population means based on the pseudo empirical likelihood method are derived. For variance estimation, we consider a bootstrap procedure and its consistency is established. Some simulation results are provided to assess the finite sample performance of the proposed estimators.