000129354 001__ 129354
000129354 005__ 20240720100832.0
000129354 0247_ $$2doi$$a10.1214/22-AOAS1719
000129354 0248_ $$2sideral$$a134239
000129354 037__ $$aART-2023-134239
000129354 041__ $$aeng
000129354 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000129354 245__ $$aSpatial quantile autoregression for season within year daily maximum temperature data
000129354 260__ $$c2023
000129354 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129354 5203_ $$aRegression 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.
000129354 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000129354 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000129354 590__ $$a1.3$$b2023
000129354 592__ $$a0.954$$b2023
000129354 591__ $$aSTATISTICS & PROBABILITY$$b65 / 168 = 0.387$$c2023$$dQ2$$eT2
000129354 593__ $$aModeling and Simulation$$c2023$$dQ1
000129354 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ1
000129354 593__ $$aStatistics and Probability$$c2023$$dQ1
000129354 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000129354 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000129354 700__ $$0(orcid)0000-0002-9052-9674$$aCebrián, Ana C.$$uUniversidad de Zaragoza
000129354 700__ $$aGelfand, Alan E.
000129354 700__ $$0(orcid)0000-0002-7974-7435$$aAbaurrea, Jesús$$uUniversidad de Zaragoza
000129354 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000129354 773__ $$g17, 3 (2023), 2305-2325$$pAnn. appl. stat$$tAnnals of Applied Statistics$$x1932-6157
000129354 8564_ $$s2648072$$uhttps://zaguan.unizar.es/record/129354/files/texto_completo.pdf$$yVersión publicada
000129354 8564_ $$s2266237$$uhttps://zaguan.unizar.es/record/129354/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000129354 909CO $$ooai:zaguan.unizar.es:129354$$particulos$$pdriver
000129354 951__ $$a2024-07-19-18:43:20
000129354 980__ $$aARTICLE