An improved Bayes empirical Bayes estimator.
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 bayesiano de problemas de decisión con valoración difusa de las consecuencias
En este trabajo se presenta un modelo matemático general y operativo para los problemas de decisión unietápicos cuyas consecuencias se cuantifican mediante números difusos. Ese modelo va a permitir establecer los fundamentos de las utilidades difusas mediante un desarrollo axiomático, y generalizar las formas normal y extensiva del análisis bayesiano dando condiciones para la equivalencia de las mismas. Se examinará también la particularización del análisis bayesiano en forma extensiva a la estimación...
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).
Approximately Optimum Two-Stage Sampling Plans.
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...
Asymptotic optimality of experimental designs in estimating a product of means.
Asymptotically minimax estimation of a constrained Poisson vector via polydisc transforms
Asymptotically minimax estimation of order-constrained parameters and eigenfunctions of the laplacian on the ball
Bayes and empirical bayes tests for the life parameter
We study the one-sided testing problem for the exponential distribution via the empirical Bayes (EB) approach. Under a weighted linear loss function, a Bayes test is established. Using the past samples, we construct an EB test and exhibit its optimal rate of convergence. When the past samples are not directly observable, we work out an EB test by using the deconvolution kernel method and obtain its asymptotic optimality.
Bayes robustness via the Kolmogorov metric
An upper bound for the Kolmogorov distance between the posterior distributions in terms of that between the prior distributions is given. For some likelihood functions the inequality is sharp. Applications to assessing Bayes robustness are presented.
Bayes sequential estimation procedures for exponential-type processes
The Bayesian sequential estimation problem for an exponential family of processes is considered. Using a weighted square error loss and observing cost involving a linear function of the process, the Bayes sequential procedures are derived.
Bayes sharpening of imprecise information
A complete algorithm is presented for the sharpening of imprecise information, based on the methodology of kernel estimators and the Bayes decision rule, including conditioning factors. The use of the Bayes rule with a nonsymmetrical loss function enables the inclusion of different results of an under- and overestimation of a sharp value (real number), as well as minimizing potential losses. A conditional approach allows to obtain a more precise result thanks to using information entered as the...
Bayes unbiased estimation in a model with two variance components
In the paper an explicit expression for the Bayes invariant quadratic unbiased estimate of the linear function of the variance components is presented for the mixed linear model , , with the unknown variance componets in the normal case. The matrices , may be singular. Applications to two examples of the analysis of variance are given.
Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)
Structural change for the Koyck Distributed Lag Model is analyzed through the Bayesian approach. The posterior distribution of the break point is derived with the use of the normal-gamma prior density and the break point, ν, is estimated by the value that attains the Highest Posterior Probability (HPP). Simulation study is done using R. Given the parameter values ϕ = 0.2 and λ = 0.3, the full detection of the structural change when σ² = 1 is generally attained at ν + 1. The after...
Bayesian inference in group judgement formulation and decision making using qualitative controlled feedback.
This paper considers the problem of making statistical inferences about group judgements and group decisions using Qualitative Controlled Feedback, from the Bayesian point of view. The qualitative controlled feedback procedure was first introduced by Press (1978), for a single question of interest. The procedure in first reviewed here including the extension of the model to the multiple question case. We develop a model for responses of the panel on each stage. Many questions are treated simultaneously...
Bayesian methods in hydrology: a review.
Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.
Bayesian testing of fuzzy parametric hypotheses from fuzzy information