Optimal Estimating Equation for Logistic Regression with Linked Data

TL;DR

This paper introduces an optimal estimating equation for logistic regression with linked data that accounts for false positives, achieving lower variance in coefficient estimates in large samples.

Contribution

It develops a new estimating equation that improves variance efficiency over previous methods for linked data logistic regression.

Findings

Reduces variance of coefficient estimates in large samples.

Accounts for false positives in linked data.

Improves estimation accuracy over existing methods.

Abstract

We propose an optimal estimating equation for logistic regression with linked data while accounting for false positives. It builds on a previous solution but estimates the regression coefficients with a smaller variance, in large samples.