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Displaying similar documents to “Bounds for distribution functions of sums of squares and radial errors.”

Extreme distribution functions of copulas

Manuel Úbeda-Flores (2008)

Kybernetika

Similarity:

In this paper we study some properties of the distribution function of the random variable C(X,Y) when the copula of the random pair (X,Y) is M (respectively, W) – the copula for which each of X and Y is almost surely an increasing (respectively, decreasing) function of the other –, and C is any copula. We also study the distribution functions of M(X,Y) and W(X,Y) given that the joint distribution function of the random variables X and Y is any copula.

On sums of dependent uniformly distributed random variables.

Claudi Alsina, Eduard Bonet (1979)

Stochastica

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We study and solve several functional equations which yield necessary and sufficient conditions for the sum of two uniformly distributed random variables to be uniformly distributed.

Multivariate probability integral transformation: application to maximum likelihood estimation.

Abderrahmane Chakak, Layachi Imlahi (2001)

RACSAM

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Sea (X, X) un vector aleatorio con una función de distribución F. La transformación integral de la probabilidad (pit) es la variable aleatoria unidimensional P = F(X, X). La expresion de su función de distribución, y un algoritmo de simulación en términos de la función cuantil, dada por Chakak et al [2000], cuando la distribución es absolumente continua, son extendidas a distribuciones que pueden tener singularidades. La estimación de máxima verosimilitud del parámetro de dependencia...

Shuffles of Min.

Piotr Mikusinski, Howard Sherwood, Michael D. Taylor (1992)

Stochastica

Similarity:

Copulas are functions which join the margins to produce a joint distribution function. A special class of copulas called shuffles of Min is shown to be dense in the collection of all copulas. Each shuffle of Min is interpreted probabilistically. Using the above-mentioned results, it is proved that the joint distribution of any two continuously distributed random variables X and Y can be approximated uniformly, arbitrarily closely by the joint distribution of another pair X* and Y* each...