000126315 001__ 126315
000126315 005__ 20241125101155.0
000126315 0247_ $$2doi$$a10.18637/jss.v106.i05
000126315 0248_ $$2sideral$$a133632
000126315 037__ $$aART-2023-133632
000126315 041__ $$aeng
000126315 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000126315 245__ $$aRecordtest: an R package to analyze non-stationarity in the extremes based on record-breaking events
000126315 260__ $$c2023
000126315 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126315 5203_ $$aThe study of non-stationary behavior in the extremes is important to analyze data in environmental sciences, climate, finance, or sports. As an alternative to the classical extreme value theory, this analysis can be based on the study of record-breaking events. The R package RecordTest provides a useful framework for non-parametric analysis of non-stationary behavior in the extremes, based on the analysis of records. The underlying idea of all the non-parametric tools implemented in the package is to use the distribution of the record occurrence under series of independent and identically distributed continuous random variables, to analyze if the observed records are compatible with that behavior. Two families of tests are implemented. The first only requires the record times of the series, while the second includes more powerful tests that join the information from different types of records: upper and lower records in the forward and backward series. The package also offers functions that cover all the steps in this type of analysis such as data preparation, identification of the records, exploratory analysis, and complementary graphical tools. The applicability of the package is illustrated with the analysis of the effect of global warming on the extremes of the daily maximum temperature series in Zaragoza, Spain.
000126315 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000126315 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126315 590__ $$a5.4$$b2023
000126315 592__ $$a2.709$$b2023
000126315 591__ $$aSTATISTICS & PROBABILITY$$b4 / 168 = 0.024$$c2023$$dQ1$$eT1
000126315 593__ $$aSoftware$$c2023$$dQ1
000126315 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b30 / 170 = 0.176$$c2023$$dQ1$$eT1
000126315 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ1
000126315 593__ $$aStatistics and Probability$$c2023$$dQ1
000126315 594__ $$a10.7$$b2023
000126315 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126315 700__ $$0(orcid)0000-0002-9052-9674$$aCebrián, Ana C.$$uUniversidad de Zaragoza
000126315 700__ $$0(orcid)0000-0002-0174-789X$$aAsín, Jesús$$uUniversidad de Zaragoza
000126315 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000126315 773__ $$g106, 5 (2023), 1-28$$pJournal of Statistical Software$$tJournal of Statistical Software$$x1548-7660
000126315 8564_ $$s922097$$uhttps://zaguan.unizar.es/record/126315/files/texto_completo.pdf$$yVersión publicada
000126315 8564_ $$s1649864$$uhttps://zaguan.unizar.es/record/126315/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126315 909CO $$ooai:zaguan.unizar.es:126315$$particulos$$pdriver
000126315 951__ $$a2024-11-22-12:08:46
000126315 980__ $$aARTICLE