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Métodos de obtención de la información esperada global.

Ernesto Veres Ferrer (1983)

Trabajos de Estadística e Investigación Operativa

En este trabajo se acomete una generalización de la definición de Shannon-Lindley para la información esperada proporcionada por un experimento que presupone la existencia de estratificación en el espacio muestral. Ante la evidente dificultad de cálculo de la información esperada en la situación planteada -dificultad que se deriva de la existencia de un vector como parámetro de interés y de un resultado muestral que es un conjunto de muestras obtenidas de poblaciones distintas- en este artículo...

Minimax and bayes estimation in deconvolution problem*

Mikhail Ermakov (2008)

ESAIM: Probability and Statistics

We consider a deconvolution problem of estimating a signal blurred with a random noise. The noise is assumed to be a stationary Gaussian process multiplied by a weight function function εh where h ∈ L2(R1) and ε is a small parameter. The underlying solution is assumed to be infinitely differentiable. For this model we find asymptotically minimax and Bayes estimators. In the case of solutions having finite number of derivatives similar results were obtained in [G.K. Golubev and R.Z. Khasminskii,...

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.

Minimax Prediction for the Multinomial and Multivariate Hypergeometric Distributions

Alicja Jokiel-Rokita (1998)

Applicationes Mathematicae

A problem of minimax prediction for the multinomial and multivariate hypergeometric distribution is considered. A class of minimax predictors is determined for estimating linear combinations of the unknown parameter and the random variable having the multinomial or the multivariate hypergeometric distribution.

Minimax prediction under random sample size

Alicja Jokiel-Rokita (2002)

Applicationes Mathematicae

A class of minimax predictors of random variables with multinomial or multivariate hypergeometric distribution is determined in the case when the sample size is assumed to be a random variable with an unknown distribution. It is also proved that the usual predictors, which are minimax when the sample size is fixed, are not minimax, but they remain admissible when the sample size is an ancillary statistic with unknown distribution.

Minimum distance estimator for a hyperbolic stochastic partial differentialequation

Vincent Monsan, Modeste N'zi (2000)

Applicationes Mathematicae

We study a minimum distance estimator in L 2 -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.

Currently displaying 21 – 40 of 70