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Las medidas de f*-divergencia en el diseño secuencial de experimentos en un contexto bayesiano.

Domingo Morales, Leandro Pardo, Vicente Quesada (1986)

Trabajos de Estadística

Se presenta un método de selección secuencial de un número fijo de experimentos a partir de las medidas de f*-divergencia introducidas por Csiszar (1967). Este trabajo es similar al desarrollado por De Groot (1970) con funciones de incertidumbre; sin embargo, no sólo se considera el problema de espacio paramétrico finito, sino que se estudia además el caso de espacio paramétrico infinito.

Le modèle bayésien

Philippe Caillot, Françoise Martin (1972)

Annales de l'I.H.P. Probabilités et statistiques

Les P-values comme votes d'experts

Guy Morel (2010)

ESAIM: Probability and Statistics

The p-values are often implicitly used as a measure of evidence for the hypotheses of the tests. This practice has been analyzed with different approaches. It is generally accepted for the one-sided hypothesis problem, but it is often criticized for the two-sided hypothesis problem. We analyze this practice with a new approach to statistical inference. First we select good decision rules without using a loss function, we call them experts. Then we define a probability distribution on the space...

Limits of Bayesian decision related quantities of binomial asset price models

Wolfgang Stummer, Wei Lao (2012)

Kybernetika

We study Bayesian decision making based on observations X n , t : t { 0 , T n , 2 T n , ... , n T n } ( T > 0 , n ) of the discrete-time price dynamics of a financial asset, when the hypothesis a special n -period binomial model and the alternative is a different n -period binomial model. As the observation gaps tend to zero (i. e. n ), we obtain the limits of the corresponding Bayes risk as well as of the related Hellinger integrals and power divergences. Furthermore, we also give an example for the “non-commutativity” between Bayesian statistical and...

Minimax mutual prediction

Stanisław Trybuła (2000)

Applicationes Mathematicae

The problems of minimax mutual prediction are considered for binomial and multinomial random variables and for sums of limited random variables with unknown distribution. For the loss function being a linear combination of quadratic losses minimax mutual predictors are determined where the parameters of predictors are obtained by numerical solution of some equations.

Minimax mutual prediction of multinomial random variables

Stanisław Trybuła (2003)

Applicationes Mathematicae

The problem of minimax mutual prediction is considered for multinomial random variables with the loss function being a linear combination of quadratic losses connected with prediction of particular variables. The basic parameter of the minimax mutual predictor is determined by numerical solution of some equation.

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