HIGHLIGHTS
- who: Jorge Castillo-Mateo and colleagues from the Department of Statistical Methods, University of Zaragoza, Zaragoza, Spain have published the research work: Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature, in the Journal: Mathematics 2023, 11, 759. of /2023/
- what: The authors propose a method for selection based on a stochastic search selection (SSVS) algorithm proposed for posterior computation. The aim of this work is to develop a new Bayesian variable selection method in the generalized extreme value (GEV) modeling framework to study extreme events. The authors implement an adaptive Metropolis . . .
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