000109477 001__ 109477
000109477 005__ 20240319080947.0
000109477 0247_ $$2doi$$a10.1007/s00477-021-02122-w
000109477 0248_ $$2sideral$$a125904
000109477 037__ $$aART-2022-125904
000109477 041__ $$aeng
000109477 100__ $$0(orcid)0000-0002-9052-9674$$aCebrián, Ana C.$$uUniversidad de Zaragoza
000109477 245__ $$aRecord tests to detect non-stationarity in the tails with an application to climate change
000109477 260__ $$c2022
000109477 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109477 5203_ $$aThe analysis of trends and other non-stationary behaviours at the extremes of a series is an important problem in global warming. This work proposes and compares several statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so it is important to obtain all the possible evidence from the available data. First, different statistics based on upper records are proposed, and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found that these joint tests are clearly more powerful. The suggested tests are specifically useful in analysing the effect of global warming in the extremes, for example, of daily temperature. They have a high power to detect weak trends and can be widely applied since they are non-parametric. The proposed statistics join the information of M independent series, which is useful given the necessary split of the series to arrange the data. This arrangement solves the usual problems of climate series (seasonality and serial correlation) and provides more series to find evidence. These tools are used to analyse the effect of global warming on the extremes of daily temperature in Madrid.
000109477 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/MTM2017-83812-P
000109477 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109477 590__ $$a4.2$$b2022
000109477 592__ $$a0.814$$b2022
000109477 591__ $$aSTATISTICS & PROBABILITY$$b10 / 125 = 0.08$$c2022$$dQ1$$eT1
000109477 593__ $$aEnvironmental Science (miscellaneous)$$c2022$$dQ1
000109477 591__ $$aWATER RESOURCES$$b27 / 103 = 0.262$$c2022$$dQ2$$eT1
000109477 593__ $$aEnvironmental Engineering$$c2022$$dQ1
000109477 591__ $$aENGINEERING, CIVIL$$b37 / 139 = 0.266$$c2022$$dQ2$$eT1
000109477 593__ $$aWater Science and Technology$$c2022$$dQ1
000109477 591__ $$aENVIRONMENTAL SCIENCES$$b104 / 275 = 0.378$$c2022$$dQ2$$eT2
000109477 593__ $$aSafety, Risk, Reliability and Quality$$c2022$$dQ1
000109477 591__ $$aENGINEERING, ENVIRONMENTAL$$b28 / 55 = 0.509$$c2022$$dQ3$$eT2
000109477 593__ $$aEnvironmental Chemistry$$c2022$$dQ2
000109477 594__ $$a6.5$$b2022
000109477 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109477 700__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000109477 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000109477 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000109477 773__ $$g36 (2022), 313–330$$pStoch. environ. res. risk assess.$$tSTOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT$$x1436-3240
000109477 8564_ $$s1084554$$uhttps://zaguan.unizar.es/record/109477/files/texto_completo.pdf$$yVersión publicada
000109477 8564_ $$s2325402$$uhttps://zaguan.unizar.es/record/109477/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109477 909CO $$ooai:zaguan.unizar.es:109477$$particulos$$pdriver
000109477 951__ $$a2024-03-18-12:44:14
000109477 980__ $$aARTICLE