000125278 001__ 125278
000125278 005__ 20241125101150.0
000125278 0247_ $$2doi$$a10.3390/math11030759
000125278 0248_ $$2sideral$$a132953
000125278 037__ $$aART-2023-132953
000125278 041__ $$aeng
000125278 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo Mateo, Jorge$$uUniversidad de Zaragoza
000125278 245__ $$aBayesian variable selection in generalized extreme value regression: modeling annual maximum temperature
000125278 260__ $$c2023
000125278 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125278 5203_ $$aIn 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.
000125278 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130702B-I00
000125278 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125278 590__ $$a2.3$$b2023
000125278 592__ $$a0.475$$b2023
000125278 591__ $$aMATHEMATICS$$b21 / 490 = 0.043$$c2023$$dQ1$$eT1
000125278 593__ $$aEngineering (miscellaneous)$$c2023$$dQ2
000125278 593__ $$aMathematics (miscellaneous)$$c2023$$dQ2
000125278 593__ $$aComputer Science (miscellaneous)$$c2023$$dQ2
000125278 594__ $$a4.0$$b2023
000125278 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125278 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000125278 700__ $$0(orcid)0000-0002-9052-9674$$aCebrián, Ana C.$$uUniversidad de Zaragoza
000125278 700__ $$0(orcid)0000-0002-8235-9580$$aMateo Lázaro, Jesús$$uUniversidad de Zaragoza
000125278 700__ $$0(orcid)0000-0002-7974-7435$$aAbaurrea, Jesús$$uUniversidad de Zaragoza
000125278 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000125278 7102_ $$12000$$2427$$aUniversidad de Zaragoza$$bDpto. Ciencias de la Tierra$$cÁrea Geodinámica Externa
000125278 773__ $$g11, 3 (2023), 759 [19 pp]$$pMathematics (Basel)$$tMathematics$$x2227-7390
000125278 8564_ $$s1401485$$uhttps://zaguan.unizar.es/record/125278/files/texto_completo.pdf$$yVersión publicada
000125278 8564_ $$s2635348$$uhttps://zaguan.unizar.es/record/125278/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125278 909CO $$ooai:zaguan.unizar.es:125278$$particulos$$pdriver
000125278 951__ $$a2024-11-22-12:06:18
000125278 980__ $$aARTICLE