Modeling Extreme Events: Sample Fraction Adaptive Choice in Parameter Estimation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

M. Manuela Neves (Autor) Outra; M. Ivette Gomes (Autor) Outra; Fernanda Figueiredo (Autor) FEP; Dora Prata Gomes (Autor) Outra

Revista

Título: Journal of Statistical Theory and Practice

Vol. 9

Páginas: 184-199

ISSN: 1559-8608

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 3 Citações

ISI Web of Science

Scopus - 23 Citações

Classificação Científica

FOS: Ciências exactas e naturais

Outras Informações

ID Authenticus: P-00A-4FM

DOI: 10.1080/15598608.2014.890984

Abstract (EN): When modeling extreme events, there are a few primordial parameters, among which we refer to the extreme value index (EVI) and the extremal index (EI). Under a framework related to large values, the EVI measures the right tail weight of the underlying distribution and the EI characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semiparametric estimators of these parameters show the same type of behavior: nice asymptotic properties but a high variance for small values of k, the number of upper order statistics used in the estimation, and a high bias for large values of k. This brings a real need for the choice of k. Choosing some well-known estimators of those two parameters, we revisit the application of a heuristic algorithm for the adaptive choice of k. A simulation study illustrates the performance of the proposed algorithm.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16