An Introduction to Copulas (Springer Series in Statistics) by Roger B. Nelsen PDF

By Roger B. Nelsen

ISBN-10: 0387286594

ISBN-13: 9780387286594

ISBN-10: 0387286780

ISBN-13: 9780387286785

ISBN-10: 1441921095

ISBN-13: 9781441921093

The research of copulas and their function in data is a brand new yet vigorously becoming box. during this e-book the scholar or practitioner of records and likelihood will locate discussions of the basic homes of copulas and a few in their basic functions. The functions contain the learn of dependence and measures of organization, and the development of households of bivariate distributions. This e-book is acceptable as a textual content or for self-study.

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Additional resources for An Introduction to Copulas (Springer Series in Statistics)

Example text

X1, X2 ,…, Xn are independent if and only if the n-copula of X1, X2 ,…, Xn is P n , and 2. each of the random variables X1, X2 ,…, Xn is almost surely a strictly increasing function of any of the others if and only if the ncopula of X1, X2 ,…, Xn is M n . 34 (a) Show that the ( n -1) -margins of an n-copula are ( n -1) copulas. ] (b) Show that if C is an n-copula, n ≥ 3, then for any k, 2 £ k < n, all ( ) k-margins of C are k-copulas. 4), and let [a,b] be an n-box in In . Prove that VM n ([a , b]) = max (min(b1 ,b2 ,L ,bn ) - max( a1 , a 2 ,L , a n ),0 ) and VP n ([a , b]) = (b1 - a1 )(b2 - a 2 ) L (bn - a n ) , and hence conclude that M n and P n are n-copulas for all n ≥ 2.

5? 28. Suppose X and Y are identically distributed continuous random variables, each symmetric about a. 2 for jointly symmetric random variables: Let X and Y be continuous random variables with joint distribution function H and margins F and G, respectively. Let (a,b) be a point in R 2 . Then (X,Y) is jointly symmetric about (a,b) if and only if H ( a + x ,b + y ) = F ( a + x ) - H ( a + x ,b - y ) for all (x,y) in R 2 and H ( a + x ,b + y ) = G (b + y ) - H ( a - x ,b + y ) for all (x,y) in R 2 .

1); similarly for y = •). Indeed, joint symmetry is a very strong condition—it is easy to show that jointly symmetric random variables must be uncorrelated when the requisite second-order moments exist (Randles and Wolfe 1979). Consequently, we will focus on radial symmetry, rather than joint symmetry, for bivariate distributions. 2) involves both the joint distribution and survival functions, it is natural to ask if copulas and survival copulas play a role in radial symmetry. The answer is provided by the next theorem.

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An Introduction to Copulas (Springer Series in Statistics) by Roger B. Nelsen


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