126315 20241125101155.0 doi 10.18637/jss.v106.i05 sideral 133632 ART-2023-133632 eng Castillo-Mateo, Jorge Universidad de Zaragoza (orcid)0000-0003-3859-0248 Recordtest: an R package to analyze non-stationarity in the extremes based on record-breaking events 2023 Access copy available to the general public Unrestricted The 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. 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/ 5.4 2023 STATISTICS & PROBABILITY 4 / 168 = 0.024 2023 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 30 / 170 = 0.176 2023 Q1 T1 2.709 2023 Software 2023 Q1 Statistics, Probability and Uncertainty 2023 Q1 Statistics and Probability 2023 Q1 10.7 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Cebrián, Ana C. Universidad de Zaragoza (orcid)0000-0002-9052-9674 Asín, Jesús Universidad de Zaragoza (orcid)0000-0002-0174-789X 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 106, 5 (2023), 1-28 Journal of Statistical Software Journal of Statistical Software 1548-7660 922097 http://zaguan.unizar.es/record/126315/files/texto_completo.pdf Versión publicada 1649864 http://zaguan.unizar.es/record/126315/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126315 articulos driver 2024-11-22-12:08:46 ARTICLE