A Continuous-Time Sequential Testing Problem.
A method of detecting changes in the behaviour of a random sequence based on the Bayes approach
A Minimal Complete Class Theorem for Decision Problems Where the Parameter Space Contains Only Finitely Many Points.
A note on almost sure convergence and convergence in measure
The present article studies the conditions under which the almost everywhere convergence and the convergence in measure coincide. An application in the statistical estimation theory is outlined as well.
A note on an open problem in the foundations of statistics.
Estudiamos un problema abierto en los fundamentos de la estadística, el de modelizar imprecisión en preferencias y creencias dentro del marco de la utilidad esperada. Este problema fundamenta el análisis de sensibilidad en estadística bayesiana. Proporcionamos una solución en términos de utilidades esperadas dependientes del estado.
A procedure for ε-comparison of means of two normal distributions
For two normal distributions N(μ₁,σ²) and N(μ₂,σ²) the problem is to decide whether |μ₁-μ₂|≤ ε for a given ε. Two decision rules are given: maximin and bayesian for σ² known and unknown.
A view on filtering of continuous data signals
Algorithm. 45. Bayes factor test. An algorithm giving tables for a non-parametric two-sample test of the Bayesian discrimination power of an observed factor
Algorithmical complexity of some statistical decision processes. I
Algorithmical complexity of some statistical decision processes. II
An admissible estimator of a lower-bounded scale parameter under squared-log error loss function
Estimation in truncated parameter space is one of the most important features in statistical inference, because the frequently used criterion of unbiasedness is useless, since no unbiased estimator exists in general. So, other optimally criteria such as admissibility and minimaxity have to be looked for among others. In this paper we consider a subclass of the exponential families of distributions. Bayes estimator of a lower-bounded scale parameter, under the squared-log error loss function with...
An algorithm for Bayes parameter identification with quadratic asymmetrical loss function
An Application of Discriminant Analysis and Artificial Neural Networks to Classification Problems
An Approach for Selecting Appropriate Methods for Treating Uncertainty in Knowledge Based Systems
An Identity Concerning a Wishart Matrix.
Análisis bayesiano de los contrastes de hipótesis paramétricos.
En este trabajo se demuestra que las soluciones clásicas a los contrastes de hipótesis paramétricos son casos particulares de la solución bayesiana a un problema de decisión con dos alternativas, en el que el incremento de utilidad por rechazar la hipótesis nula cuando es falsa es una función lineal de la discrepancia entre el modelo paramétrico aceptado y el más verosímil de los modelos compatibles con la hipótesis nula.
Análisis de distribuciones a priori bajo cierta información parcial.
Se calculan las distribuciones menos informativas cuando se utilizan como medidas de información la entropía útil y la energía informacional de Onicescu, tanto si el espacio de estados Θ es continuo (intervalo de R) como si es discreto y suponiendo que el decisor posee información acerca de algunas características de la distribución a priori (monotonías de la función de densidad, probabilidades de subconjuntos de Θ, monotonías o cotas de la razón de fallo).
Aspects of control for the normal Markov processes.
Asymptotic approximations to the Bayes posterior risk.
Asymptotic distribution of the conditional regret risk for selecting good exponential populations
In this paper empirical Bayes methods are applied to construct selection rules for the selection of all good exponential distributions. We modify the selection rule introduced and studied by Gupta and Liang [10] who proved that the regret risk converges to zero with rate . The aim of this paper is to study the asymptotic behavior of the conditional regret risk . It is shown that tends in distribution to a linear combination of independent -distributed random variables. As an application we...