Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4041
Type: Artigo Científico
Title: A semiparametric Bayesian approach to extreme value estimation
Author: Nascimento, Fernando Ferraz do
Gamerman, Dani
Lopes, Hedibert Freitas
Publication Date: 2012
Abstract: This paper is concerned with extreme value density estimation. The generalized Pareto distribution (GPD) beyond a given threshold is combined with a nonparametric estimation approach below the threshold. This semiparametric setup is shown to generalize a few existing approaches and enables density estimation over the complete sample space. Estimation is performed via the Bayesian paradigm, which helps identify model components. Estimation of all model parameters, including the threshold and higher quantiles, and prediction for future observations is provided. Simulation studies suggest a few useful guidelines to evaluate the relevance of the proposed procedures. They also provide empirical evidence about the improvement of the proposed methodology over existing approaches. Models are then applied to environmental data sets. The paper is concluded with a few directions for future work.
Keywords (english terms): Bayesian
GPD
Higher quantiles
MCMC
Threshold estimation
Nonparametric estimation of curves
Language: Inglês
CNPq Area: Ciências Sociais Aplicadas
URI: https://link.springer.com/article/10.1007/s11222-011-9270-z
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.
Appears in Collections:Coleção de Artigos Científicos

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