Displaying similar documents to “Minimax Prediction for the Multinomial and Multivariate Hypergeometric Distributions”

On some Mixture Distributions

Nakhi, Y. Ben, Kalla, S.L. (2004)

Fractional Calculus and Applied Analysis

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The aim of this paper is to establish some mixture distributions that arise in stochastic processes. Some basic functions associated with the probability mass function of the mixture distributions, such as k-th moments, characteristic function and factorial moments are computed. Further we obtain a three-term recurrence relation for each established mixture distribution.

Multivariate negative binomial distributions generated by multivariate exponential distributions

Bolesław Kopociński (1999)

Applicationes Mathematicae

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We define a multivariate negative binomial distribution (MVNB) as a bivariate Poisson distribution function mixed with a multivariate exponential (MVE) distribution. We focus on the class of MVNB distributions generated by Marshall-Olkin MVE distributions. For simplicity of notation we analyze in detail the class of bivariate (BVNB) distributions. In applications the standard data from [2] and [7] and data concerning parasites of birds from [4] are used.

Records and concomitants.

Ahsanullah, M. (2009)

Bulletin of the Malaysian Mathematical Sciences Society. Second Series

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Estimation and prediction in regression models with random explanatory variables

Nguyen Bac-Van

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The regression model X(t),Y(t);t=1,...,n with random explanatory variable X is transformed by prescribing a partition S 1 , . . . , S k of the given domain S of X-values and specifying X ( 1 ) , . . . , X ( n ) ∩ S i = X i 1 , . . . , X i α ( i ) , i = 1 , . . . , k . Through the conditioning α ( i ) = a ( i ) , i = 1 , . . . , k , X i 1 , . . . , X i α ( i ) ; i = 1 , . . . , k = x 11 , . . . , x k a ( k ) the initial model with i.i.d. pairs (X(t),Y(t)),t=1,...,n, becomes a conditional fixed-design ( x 11 , . . . , x k a ( k ) ) model Y i j , i = 1 , . . . , k ; j = 1 , . . . , a ( i ) where the response variables Y i j are independent and distributed according to the mixed conditional distribution Q ( · , x i j ) of Y given X at the observed value x i j .Afterwards, we investigate the case ( Q ) E ( Y ' | x ) = ∑ i = 1 k b i ( x ) θ i I S i ( x ) , ( Q ) D ( Y | x ) = ∑ i = 1 k d i ( x ) Σ i I S i ( x ) which...

Bayes optimal stopping of a homogeneous poisson process under linex loss function and variation in the prior

Marek Męczarski, Ryszard Zieliński (1997)

Applicationes Mathematicae

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A homogeneous Poisson process (N(t),t ≥ 0) with the intensity function m(t)=θ is observed on the interval [0,T]. The problem consists in estimating θ with balancing the LINEX loss due to an error of estimation and the cost of sampling which depends linearly on T. The optimal T is given when the prior distribution of θ is not uniquely specified.

Approximated maximum likelihood estimation of parameters of discrete stable family

Lenka Slámová, Lev B. Klebanov (2014)

Kybernetika

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In this article we propose a method of parameters estimation for the class of discrete stable laws. Discrete stable distributions form a discrete analogy to classical stable distributions and share many interesting properties with them such as heavy tails and skewness. Similarly as stable laws discrete stable distributions are defined through characteristic function and do not posses a probability mass function in closed form. This inhibits the use of classical estimation methods such...

Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro (1995)

Applicationes Mathematicae

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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.

Bayesian nonparametric estimation of hazard rate in monotone Aalen model

Jana Timková (2014)

Kybernetika

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This text describes a method of estimating the hazard rate of survival data following monotone Aalen regression model. The proposed approach is based on techniques which were introduced by Arjas and Gasbarra [4]. The unknown functional parameters are assumed to be a priori piecewise constant on intervals of varying count and size. The estimates are obtained with the aid of the Gibbs sampler and its variants. The performance of the method is explored by simulations. The results indicate...