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Article Weak Uncorrelatedness of Random Variables(Springer, 2006) Ostrovska, SNew measures of independence for n random variables, based on their moments, are studied. A scale of degrees of independence for random variables which starts with uncorrelatedness (for n = 2) and finishes at independence is constructed. The scale provides a countable linearly ordered set of measures of independence.Article Citation - WoS: 2Citation - Scopus: 2Sets of Random Variables With a Given Uncorrelation Structure(Elsevier Science Bv, 2001) Ostrovska, SLet xi (1),...,xi (n) be random variables having finite expectations. Denote i(k) := # {(j(1),...,j(k)): 1 less than or equal to j(1) < ... < j(k) less than or equal to n and E (l=1)pi (k) xi (fi) = (l=1)pi (k) E xi (h)}, k = 2,...,n. The finite sequence (i(2),...,i(n)) is called the uncorrelation structure of xi (1),...,xi (n). It is proved that for any given sequence of nonnegative integers (i(2),...,i(n)) satisfying 0 less than or equal to i(k) less than or equal to ((n)(k))and any given nondegenerate probability distributions P-1,...,P-n there exist random variables eta (1),...,eta (n) with respective distributions P-1,...,P-n such that (i(2),...,i(n)) is their uncorrelation structure. (C) 2001 Elsevier Science B.V. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 4Uncorrelatedness sets for random variables with given distributions(Amer Mathematical Soc, 2005) Ostrovska, SLet xi(1) and xi(2) be random variables having finite moments of all orders. The set U(xi(1),xi(2)) := {( j, l) is an element of N-2 : E(xi(1)(j)xi(2)(l)) = E(xi(1)(j)) E(xi(2)(l))} is said to be an uncorrelatedness set of xi(1) and xi(2). It is known that in general, an uncorrelatedness set can be arbitrary. Simple examples show that this is not true for random variables with given distributions. In this paper we present a wide class of probability distributions such that there exist random variables with given distributions from the class having a prescribed uncorrelatedness set. Besides, we discuss the sharpness of the obtained result.
