Two-step spline estimating equations for generalized additive partially linear models with large cluster sizes

TL;DR

This paper introduces a two-step spline estimation method for generalized additive partially linear models with clustered data, accommodating increasing cluster sizes and providing theoretical guarantees.

Contribution

It develops a novel two-step estimation procedure with oracle properties for large-cluster generalized additive models, supported by asymptotic theory and simulations.

Findings

Estimator performs as well as univariate estimators under oracle conditions

Asymptotic distributions and consistency are established

Finite-sample experiments confirm theoretical results

Abstract

We propose a two-step estimating procedure for generalized additive partially linear models with clustered data using estimating equations. Our proposed method applies to the case that the number of observations per cluster is allowed to increase with the number of independent subjects. We establish oracle properties for the two-step estimator of each function component such that it performs as well as the univariate function estimator by assuming that the parametric vector and all other function components are known. Asymptotic distributions and consistency properties of the estimators are obtained. Finite-sample experiments with both simulated continuous and binary response variables confirm the asymptotic results. We illustrate the methods with an application to a U.S. unemployment data set.