000112406 001__ 112406
000112406 005__ 20240319080958.0
000112406 0247_ $$2doi$$a10.1007/s13253-022-00493-3
000112406 0248_ $$2sideral$$a128501
000112406 037__ $$aART-2022-128501
000112406 041__ $$aeng
000112406 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000112406 245__ $$aSpatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragon, Spain
000112406 260__ $$c2022
000112406 5060_ $$aAccess copy available to the general public$$fUnrestricted
000112406 5203_ $$aAcknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragon, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison. Supplementary materials accompanying this paper appear online.
000112406 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/MCED/FPU-1505266$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000112406 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000112406 590__ $$a1.4$$b2022
000112406 592__ $$a0.711$$b2022
000112406 591__ $$aSTATISTICS & PROBABILITY$$b64 / 125 = 0.512$$c2022$$dQ3$$eT2
000112406 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2022$$dQ1
000112406 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b44 / 55 = 0.8$$c2022$$dQ4$$eT3
000112406 593__ $$aStatistics, Probability and Uncertainty$$c2022$$dQ2
000112406 591__ $$aBIOLOGY$$b72 / 92 = 0.783$$c2022$$dQ4$$eT3
000112406 593__ $$aStatistics and Probability$$c2022$$dQ2
000112406 593__ $$aApplied Mathematics$$c2022$$dQ2
000112406 593__ $$aEnvironmental Science (miscellaneous)$$c2022$$dQ2
000112406 594__ $$a3.2$$b2022
000112406 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000112406 700__ $$0(orcid)0000-0001-8471-3224$$aLafuente, Miguel$$uUniversidad de Zaragoza
000112406 700__ $$0(orcid)0000-0002-0174-789X$$aAsin, Jesus$$uUniversidad de Zaragoza
000112406 700__ $$0(orcid)0000-0002-9052-9674$$aCebrian, Ana C.$$uUniversidad de Zaragoza
000112406 700__ $$aGelfand, Alan E.
000112406 700__ $$0(orcid)0000-0002-7974-7435$$aAbaurrea, Jesus$$uUniversidad de Zaragoza
000112406 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000112406 773__ $$g27 (2022), 487–505$$pJ. agric. biol. environ. stat.$$tJOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS$$x1085-7117
000112406 8564_ $$s1427673$$uhttps://zaguan.unizar.es/record/112406/files/texto_completo.pdf$$yVersión publicada
000112406 8564_ $$s1469171$$uhttps://zaguan.unizar.es/record/112406/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000112406 909CO $$ooai:zaguan.unizar.es:112406$$particulos$$pdriver
000112406 951__ $$a2024-03-18-13:50:05
000112406 980__ $$aARTICLE