A new class of semi-parametric estimators of the second order parameter.

By M. I. Fraga Alves, M. Ivette Gomes, Laurens de Haan. 2003

192 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ab4ecffb303f01173f6ab3153f7a241aa0af2c49

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Citations in the corpus

Threshold Selection in Extreme Value Analysis (2016, background): “([23]) and the β–estimators in Gomes and Martins ([30]).”
Estimating Local Intrinsic Dimensionality (2015, unknown): “In other words, the 1− β confidence interval is[ ÎDX 1 + n−1/2Φ−1(1− β/2) , ÎDX 1− n−1/2Φ−1(1− β/2) ] .”
DIRECT REDUCTION OF BIAS OF THE CLASSI- CAL HILL ESTIMATOR ⁄ (2005, methodology): “1 2 ln ( M (2) n (k)/2 ) − 1 3 ln ( M (3) n (k)/6 ) if τ = 0 ,”
A simple second-order reduced bias’ tail index estimator (2007, methodology): “[11, 12] for the estimation of the second-order parameter ρ, and in ref.”
Kernel estimators of extreme level curves (2011, background): “In [1, 2, 27], the auxiliary function is supposed to be regularly varying and the estimation of the corresponding regular variation index is addressed.”
A new class of estimators of a “scale” second order parameter (2007, methodology): “2 For the estimation of ρ, we shall consider here the class of estimators introduced in Fraga Alves et al. (2003a). Those estimates depend on a tuning parameter, here denoted θ , considered non-negative but that may be straightforwardly generalised to θ ∈ R, and have the functional expression, ρ̂θ (”
Reduced-Bias Tail Index Estimators Under a Third-Order Framework (2009, background): “( 2003 ), for the full derivation of the asymptotic behavior of ρ-estimators, in Gomes et al.”
Tail index estimation for heavy‐tailed models: accommodation of bias in weighted log‐excesses (2007, background): “The new class of ρ-estimators, which was introduced in Fraga Alves et al. (2003a), enabled Gomes and Martins (2002) to use, successfully, this class of estimators in the generalized jackknife estimators that were provided by Gomes et al. (2000).”
Second-order refined peaks-over-threshold modelling for heavy-tailed distributions (2009, methodology): “Let ρ̂n be a weakly consistent estimator sequence of ρ = γτ ; see for instance Fraga Alves et al. (2003a), Fraga Alves et al. (2003b), and Peng and Qi (2004).”
A moving window approach for nonparametric estimation of the conditional tail index (2008, background): “The estimation of the function t→ ρ(t) is beyond the scope of this paper, we refer to [1, 2, 23, 7] for estimators of the second order parameter when there is no covariate information.”
Nonparametric regression estimation of conditional tails: the random covariate case (2014, background): “[24] and Caeiro and Gomes,[27] we introduce the following class of estimators for ρ(x), parameterized in terms of a tuning parameter τ ∈ R: ρ̂n(x; K, L, τ) := 3(Rn(x; K, L, τ) − 1) Rn(x; K, L, τ) − 3 ,”