Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/11078
Título: | Asymptotic and pre-asymptotic tail behavior of a power max-autoregressive model |
Autor(es): | Ferreira, Marta Susana Castro, Luisa Canto e |
Palavras-chave: | Markov Chains Extreme value theory Dependence conditions Auto-asymptotic-tail-dependence function Tail index Extremal index |
Data: | 2010 |
Editora: | Probstat Forum |
Revista: | Probstat Forum |
Citação: | FERREIRA, Marta; CASTRO, Luisa Canto e - Asymptotic and pre-asymptotic tail behavior of a power max-autoregressive model. "ProbStat Forum" [Em linha]. 3:08 (2010) 91-107. [Consult. 17 Nov. 2010]. Disponível em : http://probstat.org.in/PSF-0610.pdf. ISSN 0974-3235. |
Resumo(s): | Max-autoregressive models for time series data are useful when we want to make inference about rare events, mainly in areas like hydrology, geophysics and finance. Here we present a power max-autoregressive ($p$ARMAX) process, $\{X_i\}$, defined in such a way that the asymptotic tail dependence coefficient of Ledford and Tawn, computed for observations lag $m$ apart ($\eta_m$), exhibits a power decay with $m$ for larger values of $c$, the main parameter of the process, namely, $\eta_m=c^m$, $c\in(1/2,1)$. We also look at the threshold-dependent form of the extremal index, which is an important functional when extending discussions of extreme values from independent and identically distributed (i.i.d.) sequences to stationary ones. We state an approach for this functional as well as its connection with the coefficient $\eta$ for the $p$ARMAX process. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/11078 |
ISSN: | 0974-3235 |
Versão da editora: | http://probstat.org.in/ |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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MFerr_LCCastro_psf_rev_final.pdf Acesso restrito! | Documento principal | 308,23 kB | Adobe PDF | Ver/Abrir |