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.
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 unbiased estimation in a model with three variance components
In the paper necessary and sufficient conditions for the existence and an explicit expression for the Bayes invariant quadratic unbiased estimate of the linear function of the variance components are presented for the mixed linear model , , , with three unknown variance components in the normal case. An application to some examples from the analysis of variance is given.
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.
Bayes unbiased estimators of parameters of linear trend with autoregressive errors
The method of least wquares is usually used in a linear regression model for estimating unknown parameters . The case when is an autoregressive process of the first order and the matrix corresponds to a linear trend is studied and the Bayes approach is used for estimating the parameters . Unbiased Bayes estimators are derived for the case of a small number of observations. These estimators are compared with the locally best unbiased ones and with the usual least squares estimators.
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 analysis of the model of hidden periodicities
Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics
2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse...
Bayesian approach for joint longitudinal and time-to-event data with survival fraction.
Bayesian Approach to Prediction in the Presence of Outliers for Weibull Distribution.
Bayesian control charts for attributes and the large subgroup size in chart.
Bayesian estimation of AR(1) models with uniform innovations
The first-order autoregressive model with uniform innovations is considered. In this paper, we propose a family of BAYES estimators based on a class of prior distributions. We obtain estimators of the parameter which perform better than the maximum likelihood estimator.
Bayesian estimation of mixtures with dynamic transitions and known component parameters
Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant...
Bayesian estimation of number of the questions unfamiliar with the examinee in variant I of probabilistic model of double choice test
Bayesian estimation of parameter in variant I of probabilistic model of double choice test
Bayesian estimation of the 3-parameter inverse Gaussian distribution.
The three-parameter inverse Gaussian distribution is used as an alternative model for the three parameter lognormal, gamma and Weibull distributions for reliability problems. In this paper Bayes estimates of the parameters and reliability function of a three parameter inverse Gaussian distribution are obtained. Posterior variance estimates are compared with the variance of their maximum likelihood counterparts. Numerical examples are given.