129354 20241125101139.0 doi 10.1214/22-AOAS1719 sideral 134239 ART-2023-134239 eng Castillo-Mateo, Jorge Universidad de Zaragoza (orcid)0000-0003-3859-0248 Spatial quantile autoregression for season within year daily maximum temperature data 2023 Access copy available to the general public Unrestricted Regression is the most widely used modeling tool in statistics. Quantile regression offers a strategy for enhancing the regression picture beyond customary mean regression. With time-series data, we move to quantile autoregression and, finally, with spatially referenced time series, we move to space-time quantile regression. Here, we are concerned with the spatiotemporal evolution of daily maximum temperature, particularly with regard to extreme heat. Our motivating data set is 60 years of daily summer maximum temperature data over Aragón in Spain. Hence, we work with time on two scales—days within summer season across years—collected at geocoded station locations. For a specified quantile, we fit a very flexible, mixed-effects autoregressive model, introducing four spatial processes. We work with asymmetric Laplace errors to take advantage of the available conditional Gaussian representation for these distributions. Further, while the autoregressive model yields conditional quantiles, we demonstrate how to extract marginal quantiles with the asymmetric Laplace specification. Thus, we are able to interpolate quantiles for any days within years across our study region. info:eu-repo/grantAgreement/ES/DGA/E46-20R info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 1.3 2023 STATISTICS & PROBABILITY 65 / 168 = 0.387 2023 Q2 T2 0.954 2023 Modeling and Simulation 2023 Q1 Statistics, Probability and Uncertainty 2023 Q1 Statistics and Probability 2023 Q1 3.1 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Asín, Jesús Universidad de Zaragoza (orcid)0000-0002-0174-789X Cebrián, Ana C. Universidad de Zaragoza (orcid)0000-0002-9052-9674 Gelfand, Alan E. Abaurrea, Jesús Universidad de Zaragoza (orcid)0000-0002-7974-7435 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 17, 3 (2023), 2305-2325 Ann. appl. stat Annals of Applied Statistics 1932-6157 2648072 http://zaguan.unizar.es/record/129354/files/texto_completo.pdf Versión publicada 2266237 http://zaguan.unizar.es/record/129354/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:129354 articulos driver 2024-11-22-12:02:06 ARTICLE