Work

Tutorial on Bayesian Functional Regression Using Stan

Year: 2025

Type: article

Abstract: 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).

Source: Statistics in Medicine

DOI: 10.1002/sim.70265

Authors: Ziren Jiang, Ciprian Crainiceanu, Erjia Cui

Institutions: University of Minnesota, University of Minnesota System, Johns Hopkins University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,450

Funders: National Institutes of Health, National Institute on Aging, National Institute of Neurological Disorders and Stroke