The Bootstrap Methodology in Statistics of Extremes—Choice of the Optimal Sample Fraction

By M. Ivette Gomes, Orlando Oliveira. 2001

151 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/26de7e7d65448a186823e53dba1d05c8da5ae427

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Citations in the corpus

A Review of Extreme Value Threshold Estimation and Uncertainty Quantification (2012, background): “Further, Gomes & Oliveira (2001) have shown that the estimates are robust to the choice of resample size.”
Threshold Selection in Extreme Value Analysis (2016, methodology): “We mention the pioneering papers by Hall ([37]) and Hall and Welsh ([39]), and discuss the role of the bootstrap methodology in the selection of k0(n) := arg mink MSE(ξ̂k,n) (Hall, [38]; Draisma, de Haan, Peng and Themido Pereira [19]; Danielsson, de Haan, Peng and de Vries, [17]; Gomes and Oliveira”
Measuring Tail Thickness under GARCH and an Application to Extreme Exchange Rate Changes (2003, methodology): “Recent findings by Gomes and Oliveira (2001) and Matthys and”
Estimating Extreme Quantiles of Weibull Tail Distributions (2005, background): “Various approaches are proposed in the extreme index estimation context, see Guillou and Hall ( 2001 ), Drees and Kaufmann ( 1998 ), Gomes and Oliveira ( 2001 ), among others, and could be adapted to our problem.”
DIRECT REDUCTION OF BIAS OF THE CLASSI- CAL HILL ESTIMATOR ⁄ (2005, unknown): “For details on multi-sample simulation, see Gomes and Oliveira (2001).”
Comparison of Weibull tail-coefficient estimators (2011, background): “In [15], a bootstrap selection of k n is derived from such a representation.”
ESTIMATING THE EXTREME VALUE INDEX AND HIGH QUANTILES WITH EXPONENTIAL REGRESSION MODELS (2003, result): “This mis-specification is in agreement with established adaptive threshold selection methods for the Hill estimator (see e.g., Hall (1990), Drees and Kaufmann (1998), Gomes and Oliveira (2001) and Matthys and Beirlant (2000a)) and often reduces the variability of the estimation results.”
A Sturdy Reduced-Bias Extreme Quantile (VaR) Estimator (2007, methodology): “For details on multisample simulation, see Gomes and Oliveira (2001). Among the new estimators in this article and for each value of n, we underline the entries related to the one providing the smallest bias and smallest mean squared error.”
A Hill Type Estimator of the Weibull Tail-Coefficient (2004, methodology): “It is used in Häusler and Teugels (1985) to prove the asymptotic normality of the Hill estimator and in Beirlant et al.”