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- W2000678263 abstract "In extreme value theory the extremal index is a key parameter that enables a straightforward extension of the classic results for the independent case to stationary processes, measuring the degree of local dependence in the largest observations. Its estimation is important not only by itself but also because of its effect on the estimation of other parameters of extreme events. The estimators considered in the literature, despite of having good asymptotic properties, show a strong dependence on the high level un, presenting a high variance for high levels and a high bias when the level decreases. It has been seen that the bias is the dominant component of the mean squared error of the semiparametric estimators presented in the literature. Resampling techniques have been applied in situations where classical statistical procedures are difficult to use, but for a dependent setup, the resampling has to be done using blocks of observations. An adaptive resampling approach, based on block‐bootstrap and Jackknife‐After‐Bootstrap is here considered for estimating the optimal block size in order to obtain a “good” estimator of the bias. This work is still in progress and the main objective of this first stage is to estimate the bias for obtaining a more stable path for the extremal index estimates. A simulation study as well as an application to daily returns of the S&P 500 stock index is presented." @default.
- W2000678263 created "2016-06-24" @default.
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- W2000678263 date "2011-01-01" @default.
- W2000678263 modified "2023-09-25" @default.
- W2000678263 title "Resampling Methodologies and the Estimation of Parameters of Rare Events" @default.
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- W2000678263 doi "https://doi.org/10.1063/1.3637903" @default.
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