Tutorial on Bayesian Functional Regression Using Stan

Fuente: arXiv
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Hauptverfasser: Jiang, Ziren, Crainiceanu, Ciprian, Cui, Erjia
Format: Preprint
Veröffentlicht: 2025
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author Jiang, Ziren
Crainiceanu, Ciprian
Cui, Erjia
author_facet Jiang, Ziren
Crainiceanu, Ciprian
Cui, Erjia
contents This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible modeling and provide an alternative when frequentist methods are not available or may require additional development. Methods and software are illustrated using the accelerometry data from the National Health and Nutrition Examination Survey (NHANES).
format Preprint
id arxiv_https___arxiv_org_abs_2505_05633
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Tutorial on Bayesian Functional Regression Using Stan
Jiang, Ziren
Crainiceanu, Ciprian
Cui, Erjia
Methodology
Computation
This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible modeling and provide an alternative when frequentist methods are not available or may require additional development. Methods and software are illustrated using the accelerometry data from the National Health and Nutrition Examination Survey (NHANES).
title Tutorial on Bayesian Functional Regression Using Stan
topic Methodology
Computation
url https://arxiv.org/abs/2505.05633