The zero-truncated negative binomial distribution as a failure model from the Bayesian approach

The zero-truncated negative binomial distribution is considered as a failure model from the Bayesian point of view. It is assumed that the location parameter is a stochastic variable with beta as its prior distribution. Under this assumption Bayes estimators are derived for the location parameter and reliability function. By means of Monte Carlo Simulation the minimum variance unbiased estimators (MVUEs) for the parameter and reliability function are compared to the corresponding Bayes estimators.     

Bayes reliability estimation using multiple sources of priorinformation: binomial sampling

The authors develop Bayes estimators for the true binomial survival probability when there exist multiple sources of prior information. For each source of prior information, incomplete (partial) prior information is assumed to exist in the form of either a stated prior mean of p or a stated prior credibility interval on p; p is the parameter about which there is a degree of belief regarding its unknown value, i.e., p is treated as though it were the unknown value of a random variable. Both maximum entropy and maximum posterior risk criteria are used to determine a beta prior for each source. A mixture of these beta priors is then taken as the combined prior, after which Bayes theorem is used to obtain the final mixed beta posterior distribution from which the desired estimates are obtained. Two numerical examples illustrate the method     

Series-system reliability-estimation using very small binomialsamples

This investigation explored the effect of incorporating prior information into series-system reliability estimates, where the inferences are made using very small sets (less than 10 observations) of binomial test-data. To capture this effect, the performance of a set of Bayes interval estimators was compared to that of a set of classical estimators over a wide range of subsystem beta prior-distribution parameters. During a Monte Carlo simulation, the Bayes estimators tended to provide shorter interval estimators when the mean of the prior system-reliability differed from the true reliability by 20 percent of less, but the classical estimators dominated when the difference was greater. Based on these results, the authors conclude that there is no clear advantage to using Bayes interval estimation for sample sizes less than 10 unless the poor mean system reliability is believed to be within 20 percent of the true system reliability. Otherwise, the Lindstrom-Madden estimator, a useful classical alternative for very small samples, should be used     

A Bayesian Reliability Assessment of Complex Systems for Binomial Sampling

This paper presents a reliability assessment procedure that systematically combines complete system binomial test data with lower level binomial test data obtained from either partial system or component tests. The procedure uses beta prior distributions of all reliabilities, Bayes theorem, and probability moments. The result is a posterior distribution of system reliability that can be used to determine Bayes point and interval estimates. The beta prior distributions evolve from data on predecessor systems similar to the system in question and engineering knowledge about what the various test-alternatives measure.     

An Empirical Bayes Approach for the Poisson Life Distribution

A smooth empirical Bayes estimator is derived for the intensity parameter (hazard rate) in the Poisson distribution as used in life testing. The reliability function is also estimated either by using the empirical Bayes estimate of the parameter, or by obtaining the expectation of the reliability function. The behavior of the empirical Bayes procedure is studied through Monte Carlo simulation in which estimates of mean-squared errors of the empirical Bayes estimators are compared with those of conventional estimators such as minimum variance unbiased or maximum likelihood. Results indicate a significant reduction in mean-squared error of the empirical Bayes estimators over the conventional variety.     

On estimation for mixtures of two Rayleigh distribution with censoring

In life-testing and reliability estimation, the underlying failure time distribution need not be homogeneous, but can be mixture of several distinct life distribution. In this paper, we obtain estimates of the unknown parameters of a mixed two Rayleigh distribution with one parameter, using: maximum likelihood approach and Bayesian approach with censored sampling. Bayes estimators are obtained in closed form. A numerical comparison between the two approaches has been carried out.     

A practical method of binomial reliability assessment

It is explained that ξ-minimax estimators exist for the binomial family. Two ξ-minimax estimators of binomial reliability are derived whose performances and regions of superiority are discussed. Two numerical examples are presented. The estimators are admissible in the usual sense.     

Empirical Bayes Estimation in the Weibull Distribution

In part I empirical Bayes estimation procedures are introduced and employed to obtain an estimator for the unknown random scale parameter of a two-parameter Weibull distribution with known shape parameter. In part II, procedures are developed for estimating both the random scale and shape parameters. These estimators use a sequence of maximum likelihood estimates from related reliability experiments to form an empirical estimate of the appropriate unknown prior probability density function. Monte Carlo simulation is used to compare the performance of these estimators with the appropriate maximum likelihood estimator. Algorithms are presented for sequentially obtaining the reduced sample sizes required by the estimators while still providing mean squared error accuracy compatible with the use of the maximum likelihood estimators. In some cases whenever the prior pdf is a member of the Pearson family of distributions, as much as a 60% reduction in total test units is obtained. A numerical example is presented to illustrate the procedures.     

Nonparametric Bayes-risk estimation

Two nonparametric methods to estimate the Bayes risk using classified sample sets are described and compared. The first method uses the nearest neighbor error rate as an estimate to bound the Bayes risk. The second method estimates the Bayes decision regions by applying Parzen probability-density function estimates and counts errors made using these regions. This estimate is shown to be asymptotically consistent in mean square. The results of experiments with these estimators on simulated and empirical data imply that the estimators both have acceptable small-sample properties; however, small-sample convergence of both estimators depends strongly on the choice of metric and local area or window size in the measurement space.     

Estimation of parameters of life from progressively censored data using Burr-XII model

Based on progressively Type-II censored samples, the maximum likelihood, and Bayes estimators for some lifetime parameters (reliability, and hazard functions), as well as the parameters of the Burr-XII model, are derived. The Bayes estimators are obtained using both the symmetric (Squared Error, SE) loss function, and asymmetric (LINEX, and General Entropy, GE) loss functions. This was done with respect to the conjugate prior for the one shape parameter, and discrete prior for the other parameter of this model. Also the existence, uniqueness, and finiteness of the ML parameter estimates for this type of censoring are discussed. A practical example consisting of data from an accelerated test on insulating fluid reported by Nelson (1982) was used for illustration, and comparison. Finally, some numerical results using simulation study concerning different sample sizes, and progressive censoring schemes were reported.     

Reliability estimation in a generalized life-model with applicationto the Burr-XII

Estimators of the reliability function in a GLM (generalized life model) are considered. The class of the GLM includes (among others) the Weibull, Pareto, Beta, Gompertz, and Rayleigh distribution. A proper general prior density and the predictive function for general class of distribution proposed by Al-Hussaini(1999) are used to obtain the exact estimate. Also, the Bayes estimates relative to symmetric loss function (quadratic loss), and asymmetric loss function (LINEX loss, and GE loss), are obtained. Comparisons are made between those estimators and the MLE applying to the Burr-XII model using the Bayes approximation due to Lindley. Monte Carlo simulation was used     

An Empirical Bayes Approach to Reliability

A Bayesian reliability estimation technique known as the ``empirical Bayes approach' is developed which uses previous experience nce to get a Bayesian point estimator. The techniques require no knowledge of the form of the unknown prior distribution and are robust to assumptions about its form. Empirical Bayes techniques are applicable to situations in which prior, independent observations of the random variable X from the random couple (?, X) are available where ? is the observed parameter of interest distributed in accordance with the unknown prior distribution. Performance comparisons of the empirical Bayes and other well established techniques are developed by examples for the binomial, exponential, Normal, and Poisson situations which often occur in reliability problems. In all cases the empirical Bayes estimator performed better than the classical estimator in minimizing the average squared error.     

Bayes inference for a non-homogeneous Poisson process with powerintensity law [reliability]

Monte Carlo simulation is used to assess the statistical properties of some Bayes procedures in situations where only a few data on a system governed by a NHPP (nonhomogeneous Poisson process) can be collected and where there is little or imprecise prior information available. In particular, in the case of failure truncated data, two Bayes procedures are analyzed. The first uses a uniform prior PDF (probability distribution function) for the power law and a noninformative prior PDF for α, while the other uses a uniform PDF for the power law while assuming an informative PDF for the scale parameter obtained by using a gamma distribution for the prior knowledge of the mean number of failures in a given time interval. For both cases, point and interval estimation of the power law and point estimation of the scale parameter are discussed. Comparisons are given with the corresponding point and interval maximum-likelihood estimates for sample sizes of 5 and 10. The Bayes procedures are computationally much more onerous than the corresponding maximum-likelihood ones, since they in general require a numerical integration. In the case of small sample sizes, however, their use may be justified by the exceptionally favorable statistical properties shown when compared with the classical ones. In particular, their robustness with respect to a wrong assumption on the prior β mean is interesting     

The logarithmic series distribution as a failure model from the Bayesian point of view

In this presentation the logorithmic series is studied as a failure model from the Bayesian point of view. It is assumed that the location parameter behaves as a random variable with beta as its prior distribution. Based on this assumption Bayes estimators for the location parameter and reliability function are derived. By using computer simulation we compare the Bayes estimator for the parameter with the corresponding minimum variance unbiased estimator (MVUE) and the Bayes estimator for the reliability with a corresponding unbiased estimator derived from the MVUE of the probability function.     

HPD Prediction Intervals for Rayleigh Distribution

Using Jeffreys' non-informative prior, the predictive pdf of a future observation and that of the k-th order statistic of a future sample from a Rayleigh distribution have been obtained. Bayes predictive estimators and highest posterior density (HPD) prediction intervals for the future observation and the k-th order statistic are derived. A numerical example is given.     

Asymptotic Distribution of Bayes Estimators in Censored Type-I Samples From A Mixed Exponential Population

Bayes estimators in censored type-I samples, from a mixed exponential population are considered. Their large sample properties are examined by simulation. A log-normal distribution can explain adequately the asymptotic behaviour of Bayes estimators of the parameters.     

Bayesian estimation of reliability function and hazard rate

The Bayes estimates of reliability function and hazard rate function of the finite range failure model have been developed based on life tests that are terminated at a preassigned time point or after a certain number of failures have occurred, taking the order of observations into consideration. For the prior distribution of the parameter involved, the uniform, exponential and inverted gamma densities have been considered. As an example, failure data for a V600 indicator tube used in aircraft radar sets, which fit well the finite range failure model, have been considered as the current distribution for obtaining the Bayes estimates of the reliability function.     

A Bayesian estimation of reliability model using the linex loss function

In this paper the problem of Bayes estimation of the reliability and the shape parameter p of a finite range failure time model is considered (assuming scale parameter θ is known). Following Zellner [A. Zellner, J. Am. Statist. Assoc. 81, 446–451 (1986)] the asymmetric loss function is used to obtain the Bayes estimators. Efficiencies of the proposed Bayes estimators are obtained with respect to the ordinary Bayes estimators and it was found that the proposed Bayes estimators are better than the ordinary Bayes estimators for quite a wide range of parameters.     

Bayes estimators of exponential parameters utilizing a guessed guarantee with specified confidence

This paper proposes a procedure to modify the conjugate prior for the parameters of an exponential distribution in the light of an available guessed guarantee μ₀ with specified confidence c (0<c<1). The Bayes estimators under modified priors have been obtained. The proposed estimators have been compared with Bayes estimators under conjugate prior, shrinkage estimators and maximum likelihood estimators on the basis of a simulation study.     

Modified `practical Bayes-estimators' [reliability theory]

This paper presents a new formulation of `practical Bayes-estimators' (PBE) for the 2-parameter Weibull model when both parameters are unknown. Overcoming some limitations of the first formulation gave rise to this work, but the results are beyond this intent. These estimators are a tool to improve technical knowledge by using a few experimental data. In this case, the controversy about whether to use Bayes or classical methods is surmounted since estimators, like maximum likelihood, give estimates that often appear unlikely on the basis of technical knowledge of the engineers. A Monte Carlo study supports the following conclusions: if the shape parameter is greater than one, modified PBE maintain the good properties of practical Bayes estimators; otherwise the modified PBE are much better and do not suffer from the past limitation regarding the formulation of the prior interval on the shape parameter itself; and when there are very few data the modified PBE work as a filter that always improves (on average) the prior information if it is poor, or substantially confirms it if it is good. From this viewpoint, Bayes theorem allows statistics to help engineering and not vice versa