Time-varying coefficient model estimation through radial basis functions

• Published in: Journal of applied statistics Vol. 49; no. 10; pp. 2510 - 2534
• Main Authors: Sosa, Juan, Buitrago, Lina
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 27.07.2022; Taylor & Francis Ltd
• Subjects: Bayesian analysis; Bayesian inference; bootstrap; Correlation analysis; Kernel functions; longitudinal data analysis; Markov chains; Monte Carlo simulation; Radial basis function; radial kernel functions; Spline functions; Statistical analysis; Statistical methods; time-varying coefficient model; variational inference; Variational methods; radial kernel functions; longitudinal data analysis; time-varying coefficient model; bootstrap; Bayesian inference; variational inference
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2021.1910938

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.