Add to ORKGSeveral measures for dependence of two random variables are investigated in the case of given marginals and assuming positively quadrant dependence. Beyond known quantities (Spearman, Pearson correlation coefficient. etc.) new measures are introduced here and compared with the others. Approximate values of P.Q.D. bivariate distributions are calculated. A practical application in the hydrology of flood peaks is included
AbstractLet X1, …, Xp have p.d.f. g(x12 + … + xp2). It is shown that (a) X1, …, Xp are positively lo...
We develop an empirical likelihood (EL) approach to test independence of two univariate random varia...
AbstractThe local dependence function is constant for the bivariate normal distribution. Here we ide...
Several measures for dependence of two random variables are investigated in the case of given margin...
Let (X1, Y1),..., (Xn,Yn) be an independent random sample from a bivariate population with distribut...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
AbstractThe maximal correlation between a pair of σ-fields A and B becomes arbitrarily small as sup{...
AbstractIn this paper, the set of all bivariate positive quadrant dependent distributions with fixed...
Instead of the usual correlation coefficient for determining the closeness and mono- tony...
In the study of associated discrete variables, limitations on the range of the possible association ...
In several previous publications we developed an idea how probability tools can be used to measure s...
A new class of tests based on convex combination of the two statistics is proposed. These are func...
AbstractThe geometry of the set of p × q probability mass function matrices with fixed marginals is ...
AbstractLet X1, …, Xp have p.d.f. g(x12 + … + xp2). It is shown that (a) X1, …, Xp are positively lo...
We develop an empirical likelihood (EL) approach to test independence of two univariate random varia...
AbstractThe local dependence function is constant for the bivariate normal distribution. Here we ide...
Several measures for dependence of two random variables are investigated in the case of given margin...
Let (X1, Y1),..., (Xn,Yn) be an independent random sample from a bivariate population with distribut...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
There is a lot of interest in positive dependence going beyond linear correlation. In this paper thr...
We consider distributional free inference to test for positive quadrant dependence, i.e. for the pro...
AbstractThe maximal correlation between a pair of σ-fields A and B becomes arbitrarily small as sup{...
AbstractIn this paper, the set of all bivariate positive quadrant dependent distributions with fixed...
Instead of the usual correlation coefficient for determining the closeness and mono- tony...
In the study of associated discrete variables, limitations on the range of the possible association ...
In several previous publications we developed an idea how probability tools can be used to measure s...
A new class of tests based on convex combination of the two statistics is proposed. These are func...
AbstractThe geometry of the set of p × q probability mass function matrices with fixed marginals is ...
AbstractLet X1, …, Xp have p.d.f. g(x12 + … + xp2). It is shown that (a) X1, …, Xp are positively lo...
We develop an empirical likelihood (EL) approach to test independence of two univariate random varia...
AbstractThe local dependence function is constant for the bivariate normal distribution. Here we ide...