# Sequential estimation for GEE with adaptive variables and subject selection

## TL;DR

This paper introduces a sequential estimation method for GEE that adaptively selects subjects and variables, improving efficiency and variable identification in correlated data analysis.

## Contribution

It proposes a novel GEE-based sequential estimation approach with adaptive subject recruitment and variable selection, including an adaptive shrinkage feature.

## Key findings

- Method effectively identifies relevant variables during estimation.
- Demonstrates improved estimation accuracy on simulated and real data.
- Provides confidence sets with pre-specified precision.

## Abstract

Modeling correlated or highly stratified multiple-response data becomes a common data analysis task due to modern data monitoring facilities and methods. Generalized estimating equations (GEE) is one of the popular statistical methods for analyzing this kind of data. In this paper, we present a sequential estimation procedure for obtaining GEE-based estimates. In addition to the conventional random sampling, the proposed method features adaptive subject recruiting and variable selection. Moreover, we equip our method with an adaptive shrinkage property so that it can decide the effective variables during the estimation procedure and build a confidence set with a pre-specified precision for the corresponding parameters. In addition to the statistical properties of the proposed procedure, we assess our method using both simulated data and real data sets.