Pseudo Empirical Likelihood Inference for Non-Probability Survey Samples

TL;DR

This paper introduces a pseudo empirical likelihood method for analyzing non-probability survey samples, offering improved confidence interval properties and demonstrating superior performance in simulations, advancing survey sampling inference techniques.

Contribution

It proposes a novel pseudo empirical likelihood approach for non-probability samples, enhancing confidence interval properties and practical applicability.

Findings

Proposed methods yield more desirable confidence intervals.

Simulation results show superiority in binary response analysis.

Methods are asymptotically equivalent to existing estimators.

Abstract

In this paper, the authors first provide an overview of two major developments on complex survey data analysis: the empirical likelihood methods and statistical inference with non-probability survey samples, and highlight the important research contributions to the field of survey sampling in general and the two topics in particular by Canadian survey statisticians. The authors then propose new inferential procedures on analyzing non-probability survey samples through the pseudo empirical likelihood approach. The proposed methods lead to asymptotically equivalent point estimators that have been discussed in the recent literature but possess more desirable features on confidence intervals such as range-respecting and data-driven orientation. Results from a simulation study demonstrate the superiority of the proposed methods in dealing with binary response variables.