Conditions nécessaires et suffisantes de convergence L1 en probabilité de l'histogramme pour une densité
Confidence intervals for reliability functions of an exponential distribution under random censorship
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.
Consistent Estimation of a Regression with Errors in the Variables.
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.
Constructing median-unbiased estimators in one-parameter families of distributions via stochastic ordering
If θ ∈ Θ is an unknown real parameter of a given distribution, we are interested in constructing an exactly median-unbiased estimator θ̂ of θ, i.e. an estimator θ̂ such that a median Med(θ̂ ) of the estimator equals θ, uniformly over θ ∈ Θ. We shall consider the problem in the case of a fixed sample size n (nonasymptotic approach).
Construction of -variate distribution functions and applications to discrete decision models.
Construction of multivariate distributions: a review of some recent results.
The construction of multivariate distributions is an active field of research in theoretical and applied statistics. In this paper some recent developments in this field are reviewed. Specifically, we study and review the following set of methods: (a) Construction of multivariate distributions based on order statistics, (b) Methods based on mixtures, (c) Conditionally specified distributions, (d) Multivariate skew distributions, (e) Distributions based on the method of the variables in common and...
Convergence in Distribution of Minimum-Distance Estimators.
Convergence rate of the distributions of normalized maximum likelihood estimators for irregular parametric families.
Convergencia del vector de probabilidad a posteriori bajo una distribución predictiva.
La convergencia casi segura de una sucesión de variables aleatorias, con respecto a PX,Q (distribución predictiva), se estudia en relación con la convergencia casi segura, con respecto a PX,θ (para todo θ ∈ Θ), donde {PX,θ}θ ∈ Θ es una familia de modelos de probabilidad sobre el espacio muestral χ.Como consecuencia, se estudia la convergencia casi segura del vector de probabilidad a posteriori con respecto a PX,Q.
Convex densities and their financial applications
Coodness of fit test Statistics based on Riedwyl-type derivations
Copula-based grouped risk aggregation under mixed operation
This paper deals with the problem of risk measurement under mixed operation. For this purpose, we divide the basic risks into several groups based on the actual situation. First, we calculate the bounds for the subsum of every group of basic risks, then we obtain the bounds for the total sum of all the basic risks. For the dependency relationships between the basic risks in every group and all of the subsums, we give different copulas to describe them. The bounds for the aggregated risk under mixed...
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...
Corrections : «Estimation des densités : risque minimax»
Correlation analysis: Exact permutation paradigm
Coupling a branching process to an infinite dimensional epidemic process***
Branching process approximation to the initial stages of an epidemic process has been used since the 1950's as a technique for providing stochastic counterparts to deterministic epidemic threshold theorems. One way of describing the approximation is to construct both branching and epidemic processes on the same probability space, in such a way that their paths coincide for as long as possible. In this paper, it is shown, in the context of a Markovian model of parasitic infection, that coincidence...
Cramer Type Large Deviations for Studentized U-Statistics.