RT info:eu-repo/semantics/article
T1 Bayesian Variable Selection with Applications in Health Sciences
A1 Garcia Donato, Gonzalo
A1 Castellanos Nueda, María Eugenia
A1 Quirós Carretero, Alicia
A2 Matematica Aplicada
K1 Estadística
K1 Medicina. Salud
K1 Bayes factor
K1 Bayesian model averaging
K1 Censored data
K1 Conventional prior
K1 Multiplicity correction
K1 Singular models
K1 1209.08 Fundamentos de la Inferencia Estadística
K1 1209.13 Técnicas de Inferencia Estadística
K1 32 Ciencias Médicas
AB [EN] In health sciences, identifying the leading causes that govern the behaviour of a response variable is a question of crucial interest. Formally, this can be formulated as a variable selection problem. In this paper, we introduce the basic concepts of the Bayesian approach for variable selection based on model choice, emphasizing the model space prior adoption and the algorithms for sampling from the model space and for posterior probabilities approximation; and show its application to two common problems in health sciences. The first concerns a problem in the field of genetics while the second is a longitudinal study in cardiology. In the context of these applications, considerations about control for multiplicity via the prior distribution over the model space, linear models in which the number of covariates exceed the sample size, variable selection with censored data, and computational aspects are discussed. The applications presented here also have an intrinsic statistical interest as the proposed models go beyond the standard general linear model. We believe this work will broaden the access of practitioners to Bayesian methods for variable selection.
PB MDPI
LK https://hdl.handle.net/10612/20428
UL https://hdl.handle.net/10612/20428
NO García-Donato, G., Castellanos, M. E., & Quirós, A. (2021). Bayesian variable selection with applications in health sciences. Mathematics, 9(3), 1-16. https://doi.org/10.3390/MATH9030218
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 11-jun-2024