Time-varying coefficient model estimation through radial basis functions

© 2021 Informa UK Limited, trading as Taylor & Francis Group.

Détails bibliographiques
Publié dans:Journal of applied statistics. - 1991. - 49(2022), 10 vom: 07., Seite 2510-2534
Auteur principal: Sosa, Juan (Auteur)
Autres auteurs: Buitrago, Lina
Format: Article en ligne
Langue:English
Publié: 2022
Accès à la collection:Journal of applied statistics
Sujets:Journal Article Bayesian inference bootstrap longitudinal data analysis radial kernel functions time-varying coefficient model variational inference
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520 |a 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 
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650 4 |a time-varying coefficient model 
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