Displaying similar documents to “The extreme value Birnbaum-Saunders model, its moments and an application in biometry”

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.

Selection of variables in Discrete Discriminant Analysis

Anabela Marques, Ana Sousa Ferreira, Margarida G.M.S. Cardoso (2013)

Biometrical Letters

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In Discrete Discriminant Analysis one often has to deal with dimensionality problems. In fact, even a moderate number of explanatory variables leads to an enormous number of possible states (outcomes) when compared to the number of objects under study, as occurs particularly in the social sciences, humanities and health-related elds. As a consequence, classi cation or discriminant models may exhibit poor performance due to the large number of parameters to be estimated. In the present...

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...

Scaling of model approximation errors and expected entropy distances

Guido F. Montúfar, Johannes Rauh (2014)

Kybernetika

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We compute the expected value of the Kullback-Leibler divergence of various fundamental statistical models with respect to Dirichlet priors. For the uniform prior, the expected divergence of any model containing the uniform distribution is bounded by a constant 1 - γ . For the models that we consider this bound is approached as the cardinality of the sample space tends to infinity, if the model dimension remains relatively small. For Dirichlet priors with reasonable concentration parameters...

Parameter estimation of sub-Gaussian stable distributions

Vadym Omelchenko (2014)

Kybernetika

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In this paper, we present a parameter estimation method for sub-Gaussian stable distributions. Our algorithm has two phases: in the first phase, we calculate the average values of harmonic functions of observations and in the second phase, we conduct the main procedure of asymptotic maximum likelihood where those average values are used as inputs. This implies that the main procedure of our method does not depend on the sample size of observations. The main idea of our method lies in...