Time-Varying Coefficient Model Estimation Through Radial Basis Functions

TL;DR

This paper introduces a novel method for estimating time-varying coefficients using radial basis functions, comparing it with spline-based methods through extensive simulations and real AIDS study data.

Contribution

It proposes a radial kernel function approach for dynamic parameter estimation and compares it with spline methods using both frequentist and Bayesian inference.

Findings

Radial basis function method performs comparably or better than spline methods.

The approach is effective across various sample sizes and measurement scenarios.

Method successfully applied to AIDS clinical study data.

Abstract

In this paper we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.