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Activity Number: 340 - New Advances in Analysis of Social Science Research
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #323210
Title: Nonparametric Estimation for Extreme-Value Copula Functions via Constrained Spline Regressions
Author(s): Yang Li* and Yichen Qin and Siqi Xiang and Jun Yan
Companies: Renmin University of China and University of Cincinnati and Renmin University of China and University of Connecticut
Keywords: convex spline ; knots ; copulas

A new nonparametric estimation procedure is introduced for extreme-value copulas using spline regressions. By fitting a shape constrained spline regression function to the data points obtained from the rank-based transformation of the original observations, the authors provide new estimates of the Pickands dependence functions of the extreme-value copula. In order to impose the shape constraints on the spline regression, a new set of basis functions which satisfies such constraints is proposed. Compared with existing methods, the method works well in simulation and in real data analysis.

Authors who are presenting talks have a * after their name.

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