Access Restriction

Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Nonparametric Statistic ♦ Extreme Dependence Function ♦ Unifying Approach ♦ Multivariate Extreme Dependence Function ♦ Automatic Selection Method ♦ Simulation Study ♦ Important Estimator ♦ Convex Hull Technique ♦ Unknown Parameter ♦ Non-parametric Estimator ♦ Obtained Estimator ♦ Wide Range ♦ New Estimator ♦ Various Characterization
Abstract This article reviews various characterizations of a multivariate extreme dependence function A(·). The most important estimators derived from these characterizations are also sketched. Then, a unifying approach, which puts all these estimators under the same framework, is presented. This unifying approach enables us to construct new estimators and, most importantly, to propose an automatic selection method for an unknown parameter on which all the existing non-parametric estimators of A(·) depend. Constrained smoothing splines and convex hull techniques are used to force the obtained estimators to be extreme dependence functions. A simulation study comparing these estimators on a wide range of extreme dependence functions is provided.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2004-01-01