Yuri Goegebeur

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A distribution is said to be of Pareto-type if for some γ > 0 the tail quantile function U (x) := inf{y : F (y) ≥ 1 − 1/x}, x > 1, is of the form U (x) = x γ U (x), (1) with U a slowly varying function at infinity, i.e. U (tx)// U (t) → 1 as t → ∞ for all x > 0. In the analysis of Pareto-type models, the estimation of the tail parameter γ, and the(More)
We consider a possible scenario of experimental analysis on heuristics for optimization: identifying the contribution of local search components when algorithms are evaluated on the basis of solution quality attained. We discuss the experimental designs with special focus on the role of the test instances in the statistical analysis. Contrary to previous(More)
The estimation of the Pareto index in presence of covariate information is discussed. The Pareto index is modelled as a function of the explanatory variables and hence measures the tail heaviness of the conditional distribution of the response variable given this covariate information. The original response data are transformed in order to obtain(More)
A new class of estimators for the Weibull-tail coefficient is proposed. The estimators are based on linear combinations of log-spacings of the mean excess function evaluated at high levels. The asymptotic distribution of this new class of estimators is derived under some mild conditions on the weight function and a second order condition on the tail(More)