Conjugate priors for exponential-type processes with random initial conditions
The family of proper conjugate priors is characterized in a general exponential model for stochastic processes which may start from a random state and/or time.
Constraints on distributions imposed by properties of linear forms
Let (X1,Y1),...,(Xm,Ym) be m independent identically distributed bivariate vectors and L1 = β1X1 + ... + βmXm, L2 = β1X1 + ... + βmXm are two linear forms with positive coefficients. We study two problems: under what conditions does the equidistribution of L1 and L2 imply the same property for X1 and Y1, and under what conditions does the independence of L1 and L2 entail independence of X1 and Y1? Some analytical sufficient conditions are obtained and it is shown that in general they can not be...
Constraints on distributions imposed by properties of linear forms
Let be independent identically distributed bivariate vectors and , are two linear forms with positive coefficients. We study two problems: under what conditions does the equidistribution of and imply the same property for and , and under what conditions does the independence of and entail independence of and ? Some analytical sufficient conditions are obtained and it is shown that in general they can not be weakened.
Convex densities and their financial applications
Core functions and core divergences of regular distributions
On bounded or unbounded intervals of the real line, we introduce classes of regular statistical families, called Johnson families because they are obtained using generalized Johnson transforms. We study in a rigorous manner the formerly introduced concept of core function of a distribution from a Johnson family, which is a modification of the well known score function and which in a one-to-one manner represents the distribution. Further, we study Johnson parametrized families obtained by Johnson...
Defects and transformations of quasi-copulas
Six different functions measuring the defect of a quasi-copula, i. e., how far away it is from a copula, are discussed. This is done by means of extremal non-positive volumes of specific rectangles (in a way that a zero defect characterizes copulas). Based on these defect functions, six transformations of quasi-copulas are investigated which give rise to six different partitions of the set of all quasi-copulas. For each of these partitions, each equivalence class contains exactly one copula being...
Die Charakterisierung der Normalverteilung nach Gauß.
Die Verteilung der Gestorbenen um das Normalalter
Distribuciones continuas truncadas y sus funciones de medias.
En un trabajo anterior [6], se estudiaron las funciones de medias de distribuciones generales. En el presente trabajo, limitándonos a distribuciones de tipo continuo, se resuelve completamente la caracterización de las funciones de medias y el problema de inversión de la transformación funcional que se estudia.
Distribution characterization in a practical moment problem.
Distributions Characterized Through Conditional Expectations.
Distributions de type paretien et représentation des inégalités
Distributions Determined by Conditioning on a Pair of Order Statistics.
Distributions Determined by Conditioning on a Single Order Statistic.
Drought models based on Burr XII variables
Burr distributions are some of the most versatile distributions in statistics. In this paper, a drought application is described by deriving the exact distributions of U = XY and W = X/(X+Y) when X and Y are independent Burr XII random variables. Drought data from the State of Nebraska are used.
Dualité du problème des marges et ses applications
Eigenvalues of the Laguerre process as non-colliding squared Bessel processes.
Eine simultane Charakterisierung der geometrischen Verteilung und logistischen Verteilung.
Expressions for the probabilities of the bivariate Hermite distribution and related properties
Extreme distribution functions of copulas
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.