Resampling-based methodologies in statistics of extremes: environmental and financial applications (2015)
- Authors:
- Autor USP: RODRIGUES, LIGIA CARLA PINTO HENRIQUES JORGE - IME
- Unidade: IME
- Subjects: INFERÊNCIA NÃO PARAMÉTRICA; PROCESSOS ESTOCÁSTICOS; ESTATÍSTICA APLICADA
- Language: Inglês
- Imprenta:
- Source:
- Conference titles: International Conference and Advanced School Planet Earth, Portugal
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ABNT
GOMES, M. Ivette e HENRIQUES-RODRIGUES, Lígia e FIGUEIREDO, Fernanda. Resampling-based methodologies in statistics of extremes: environmental and financial applications. 2015, Anais.. Cham: Springer, 2015. . Acesso em: 19 abr. 2024. -
APA
Gomes, M. I., Henriques-Rodrigues, L., & Figueiredo, F. (2015). Resampling-based methodologies in statistics of extremes: environmental and financial applications. In Mathematics of Energy and Climate Change: International Conference and Advanced School Planet Earth, Portugal, March 21-28, 2013. Cham: Springer. -
NLM
Gomes MI, Henriques-Rodrigues L, Figueiredo F. Resampling-based methodologies in statistics of extremes: environmental and financial applications. Mathematics of Energy and Climate Change: International Conference and Advanced School Planet Earth, Portugal, March 21-28, 2013. 2015 ;[citado 2024 abr. 19 ] -
Vancouver
Gomes MI, Henriques-Rodrigues L, Figueiredo F. Resampling-based methodologies in statistics of extremes: environmental and financial applications. Mathematics of Energy and Climate Change: International Conference and Advanced School Planet Earth, Portugal, March 21-28, 2013. 2015 ;[citado 2024 abr. 19 ] - Location-invariant reduced-bias tail index estimation under a third-order framework
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- Swimming performance index based on extreme value theory
- Adaptive estimation for light-tailed models
- Corrected-Hill versus partially reduced-bias value-at-risk estimation
- Competitive estimation of the extreme value index
- Mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison
- Reduced‐bias kernel estimators of a positive extreme value index
- PORT estimation of parameters of extreme events through generalized means
- Generalized means and peaks over random thresholds value-at-risk estimation
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