Bayesian variable selection in generalized extreme value regression: modeling annual maximum temperature

Castillo Mateo, Jorge (Universidad de Zaragoza) ; Asín, Jesús (Universidad de Zaragoza) ; Cebrián, Ana C. (Universidad de Zaragoza) ; Mateo Lázaro, Jesús (Universidad de Zaragoza) ; Abaurrea, Jesús (Universidad de Zaragoza)

Resumen: In many applications, interest focuses on assessing relationships between covariates and the extremes of the distribution of a continuous response. For example, in climate studies, a usual approach to assess climate change has been based on the analysis of annual maximum data. Using the generalized extreme value (GEV) distribution, we can model trends in the annual maximum temperature using the high number of available atmospheric covariates. However, there is typically uncertainty in which of the many candidate covariates should be included. Bayesian methods for variable selection are very useful to identify important covariates. However, such methods are currently very limited for moderately high dimensional variable selection in GEV regression. We propose a Bayesian method for variable selection based on a stochastic search variable selection (SSVS) algorithm proposed for posterior computation. The method is applied to the selection of atmospheric covariates in annual maximum temperature series in three Spanish stations.

Idioma: Inglés

DOI: 10.3390/math11030759

Año: 2023

Publicado en: Mathematics 11, 3 (2023), 759 [19 pp]
ISSN: 2227-7390

Factor impacto JCR: 2.3 (2023)

Categ. JCR: MATHEMATICS rank: 21 / 489 = 0.043 (2023) - Q1 - T1

Factor impacto CITESCORE: 4.0 - Mathematics (all) (Q1) - Engineering (miscellaneous) (Q2) - Computer Science (miscellaneous) (Q2)

Factor impacto SCIMAGO: 0.475 - Computer Science (miscellaneous) (Q2) - Mathematics (miscellaneous) (Q2) - Engineering (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130702B-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Área Geodinámica Externa (Dpto. Ciencias de la Tierra)