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Distribution and Dependence-Function Estimation for Bivariate Extreme-Value Distributions
Peter Hall and Nader Tajvidi
Vol. 6, No. 5 (Oct., 2000), pp. 835-844
Published by: International Statistical Institute (ISI) and the Bernoulli Society for Mathematical Statistics and Probability
Stable URL: http://www.jstor.org/stable/3318758
Page Count: 10
You can always find the topics here!Topics: Estimators, Statistical estimation, Estimation methods, Data smoothing, Statism, Estimate reliability, Statistics, Consistent estimators, Simulations, Mathematical functions
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Two new methods are suggested for estimating the dependence function of a bivariate extreme-value distribution. One is based on a multiplicative modification of an earlier technique proposed by Pickands, and the other employs spline smoothing under constraints. Both produce estimators that satisfy all the conditions that define a dependence function, including convexity and the restriction that its curve lie within a certain triangular region. The first approach does not require selection of smoothing parameters; the second does, and for that purpose we suggest explicit tuning methods, one of them based on cross-validation.
Bernoulli © 2000 Bernoulli Society for Mathematical Statistics and Probability