Estimation of population-level summaries in general semiparametric repeated measures regression models

TL;DR

This paper develops kernel-based methods for estimating population summaries in a broad class of semiparametric repeated measures models, applicable to diverse data types like longitudinal and clustered data.

Contribution

It introduces a unified framework for estimating population-level quantities in semiparametric models using plug-in kernel estimators and derives their asymptotic properties.

Findings

Successful application to hemoglobin data demonstrating the method.

Asymptotic distribution formulas for the estimators.

Versatile approach applicable to various semiparametric models.

Abstract

This paper considers a wide family of semiparametric repeated measures regression models, in which the main interest is on estimating population-level quantities such as mean, variance, probabilities etc. Examples of our framework include generalized linear models for clustered/longitudinal data, among many others. We derive plug-in kernel-based estimators of the population level quantities and derive their asymptotic distribution. An example involving estimation of the survival function of hemoglobin measures in the Kenya hemoglobin study data is presented to demonstrate our methodology.