Mean-of-order-p location-invariant extreme value index estimation (2016)
- Authors:
- Autor USP: RODRIGUES, LIGIA CARLA PINTO HENRIQUES JORGE - IME
- Unidade: IME
- Subjects: ESTATÍSTICA; INFERÊNCIA NÃO PARAMÉTRICA
- Keywords: bootstrap and/or heuristic threshold selection; heavy tails; location/scale invariant semi-parametric estimation; Monte-Carlo simulation; optimal levels; statistics of extremes
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Revstat Statistical Journal
- ISSN: 1645-6726
- Volume/Número/Paginação/Ano: v. 14, n. 3, p. 273-296, 2016
-
ABNT
GOMES, M. Ivette e HENRIQUES-RODRIGUES, Lígia e MANJUNAT, B. G. Mean-of-order-p location-invariant extreme value index estimation. Revstat Statistical Journal, v. 14, n. 3, p. 273-296, 2016Tradução . . Disponível em: https://www.ine.pt/revstat/pdf/rs160303.pdf. Acesso em: 31 maio 2024. -
APA
Gomes, M. I., Henriques-Rodrigues, L., & Manjunat, B. G. (2016). Mean-of-order-p location-invariant extreme value index estimation. Revstat Statistical Journal, 14( 3), 273-296. Recuperado de https://www.ine.pt/revstat/pdf/rs160303.pdf -
NLM
Gomes MI, Henriques-Rodrigues L, Manjunat BG. Mean-of-order-p location-invariant extreme value index estimation [Internet]. Revstat Statistical Journal. 2016 ; 14( 3): 273-296.[citado 2024 maio 31 ] Available from: https://www.ine.pt/revstat/pdf/rs160303.pdf -
Vancouver
Gomes MI, Henriques-Rodrigues L, Manjunat BG. Mean-of-order-p location-invariant extreme value index estimation [Internet]. Revstat Statistical Journal. 2016 ; 14( 3): 273-296.[citado 2024 maio 31 ] Available from: https://www.ine.pt/revstat/pdf/rs160303.pdf - Location-invariant reduced-bias tail index estimation under a third-order framework
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- Mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison
- Reduced‐bias kernel estimators of a positive extreme value index
- Resampling-based methodologies in statistics of extremes: environmental and financial applications
- Corrected-Hill versus partially reduced-bias value-at-risk estimation
- Adaptive estimation for light-tailed models
- PORT estimation of parameters of extreme events through generalized means
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