Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
AbstractFor a given d-dimensional distribution function (df) H we introduce the class of dependence measures μ(H, Q) = −E{n H(Z1, . . . , Zd)}, where the random vector (Z1, . . . , Zd) has df Q which has the same marginal dfs as H. If both H and Q are max-stable dfs, we show that for a df F in the max-domain of attraction of H, this dependence measure explains the extremal dependence exhibited by F. Moreover, we prove that μ(H, Q) is the limit of the probability that the maxima of a random sample from F is marginally dominated by some random vector with df in the max-domain of attraction of Q. We show a similar result for the complete domination of the sample maxima which leads to another measure of dependence denoted by λ(Q, H). In the literature λ(H, H), with H a max-stable df, has been studied in the context of records, multiple maxima, concomitants of order statistics and concurrence probabilities. It turns out that both μ(H, Q) and λ(Q, H) are closely related. If H is max-stable we derive useful representations for both μ(H, Q) and λ(Q, H). Our applications include equivalent conditions for H to be a product df and F to have asymptotically independent components.
Dependence Modeling – de Gruyter
Published: May 1, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.