Algorithms for determining the model structure of a controlled system
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 asymptotic optimal design.
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 supervivencia basado en el modelo de Rayleigh: addendum.
In the printed version of the paper Bayesian survival analysis based on the Rayleigh model (Trabajos de Estadística Vol. 5, no. 1, 1990), figures num. 1, 2 and 3 mentioned on page 91 were not printed with the paper. That may create confusion and problems for the readers in understanding the conclusions, as in the absence of figures the paper is incomplete. For this reason we publish the figures in this issue.
Análisis Bayesiano del modelo ARE(1) con coeficiente independiente
En este trabajo se introduce el modelo ARE(I) con indicador de nivel mínimo J.l, parámetro que generaliza el modelo ARO) con errores exponenciales y se analiza desde un punto de vista bayesiano, obteniéndose una familia de distribuciones conjugadas para el hiperparámetro que describe el modelo.
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).
Another Look at the Naive Estimator in a Regression Model.
Aspects of control for the normal Markov processes.
Assessing influence in survival data with a cured fraction and covariates.
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to...
Asymptotic behaviour of a BIPF algorithm with an improper target
The BIPF algorithm is a Markovian algorithm with the purpose of simulating certain probability distributions supported by contingency tables belonging to hierarchical log-linear models. The updating steps of the algorithm depend only on the required expected marginal tables over the maximal terms of the hierarchical model. Usually these tables are marginals of a positive joint table, in which case it is well known that the algorithm is a blocking Gibbs Sampler. But the algorithm makes sense even...
Asymptotic optimality of sequential designs for estimation.
Asymptotic optimality of the Bayes estimator on differentiable in quadratic mean models.
Asymptotically minimax estimation of order-constrained parameters and eigenfunctions of the laplacian on the ball
BAYES Estimation for the Variance of a Finite Population.
Bayes estimation of the reliability function and hazard rate of a Weibull failure time distribution.
Given the recorded life times from a Weibull distribution, Bayes estimates of the reliability function and hazard rate are obtained using the posterior distributions and some recent results on Bayesian approximations due to Lindley (1980). Based on a Monte Carlo study, these estimates are compared with their maximum likelihood counterparts.
Bayes optimal stopping of a homogeneous poisson process under linex loss function and variation in the prior
A homogeneous Poisson process (N(t),t ≥ 0) with the intensity function m(t)=θ is observed on the interval [0,T]. The problem consists in estimating θ with balancing the LINEX loss due to an error of estimation and the cost of sampling which depends linearly on T. The optimal T is given when the prior distribution of θ is not uniquely specified.
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 for an Exponential Family of Processes: A Discrete Time Approach.