Displaying similar documents to “Choosing the best φ -divergence goodness-of-fit statistic in multinomial sampling with linear constraints”

Distributivity of strong implications over conjunctive and disjunctive uninorms

Daniel Ruiz-Aguilera, Joan Torrens (2006)

Kybernetika

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This paper deals with implications defined from disjunctive uninorms U by the expression I ( x , y ) = U ( N ( x ) , y ) where N is a strong negation. The main goal is to solve the functional equation derived from the distributivity condition of these implications over conjunctive and disjunctive uninorms. Special cases are considered when the conjunctive and disjunctive uninorm are a t -norm or a t -conorm respectively. The obtained results show a lot of new solutions generalyzing those obtained in previous works...

A modification of the Hartung-Knapp confidence interval on the variance component in two-variance-component models

Barbora Arendacká (2007)

Kybernetika

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We consider a construction of approximate confidence intervals on the variance component σ 1 2 in mixed linear models with two variance components with non-zero degrees of freedom for error. An approximate interval that seems to perform well in such a case, except that it is rather conservative for large σ 1 2 / σ 2 , was considered by Hartung and Knapp in [hk]. The expression for its asymptotic coverage when σ 1 2 / σ 2 → ∞ suggests a modification of this interval that preserves some nice properties of the original...

Stochastic control optimal in the Kullback sense

Jan Šindelář, Igor Vajda, Miroslav Kárný (2008)

Kybernetika

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The paper solves the problem of minimization of the Kullback divergence between a partially known and a completely known probability distribution. It considers two probability distributions of a random vector ( u 1 , x 1 , ... , u T , x T ) on a sample space of 2 T dimensions. One of the distributions is known, the other is known only partially. Namely, only the conditional probability distributions of x τ given u 1 , x 1 , ... , u τ - 1 , x τ - 1 , u τ are known for τ = 1 , ... , T . Our objective is to determine the remaining conditional probability distributions of u τ ...