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