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.     

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.     

Bayes Interval Estimation for the Shape Parameter of the Power Distribution

The power distribution is considered as failure model and uses a square-error loss function. Bayes credibility interval estimators for the shape parameter have been obtained assuming 1) the following priors for the shape parameter: Jeffrey's invariant prior, gamma, and inverted gamma; 2) the following priors for reliability: beta and log gamma function. It is straightforward to obtain estimators for reliability when the estimators for the shape parameter are known.     

A Bayesian Approach to Parameter and Reliability Estimation in the Poisson Distribution

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.     

On Bayes Estimation of Reliability for the Birnbaum-Saunders Fatigue Life Model

Estimation of reliability for the Birnbaum-Saunders fatigue life distribution is considered. The scale parameter is also the median lifetime, and assuming that the scale parameter is known, Bayes estimators of the reliability function are obtained for a family of proper conjugate priors as well as for Jeffreys' vague prior for the shape parameter. When both parameters are unknown, a modified Bayes estimator of reliability is proposed using a moment estimator of the scale parameter. In addition to being computationally simpler than the MLE of reliability, Monte Carlo simulations for small samples show that the modified Bayes estimator is better than the MME for all values of the shape parameter and as good as the MLE for small values of the shape parameter in the sense of root mean squared errors.     

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.     

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.     

Minimum Expected Loss Estimators of Reliability and Parameters of Certain Lifetime Distributions

The estimators of reliability and parameters of certain lifetime distributions which are widely used in reliability, repairability, and maintainability are obtained by using a different form of loss function and minimizing the s-expected loss with respect to the posterior distribution. These estimators are called MELO estimators. The applications, and the comparison between MELO, Bayes, and Maximum likelihood estimators are discussed.     

Bayes credibility intervals for the left-truncated exponential distribution

In this paper, a Bayes approach for statistical inference on life characteristics is proposed, when the underlying lifetime distribution has the left-truncated exponential density function. The proposed Bayes procedure provides credibility intervals on several life characteristics of great interest to the applied reliability engineer, when the experimental data are collected under a randomly censored sampling. The prior technical knowledge is expressed in the form of a prior density on the reliability level at a prefixed time in conjunction with an upper bound on the location parameter. The statistical properties of the proposed Bayes procedure are compared, via Monte Carlo simulation, with those of the Bayes procedure under the noninformative prior, when both correct and uncorrect prior information on the reliability is available. A numerical example is used for illustration and comparison.     

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.     

Bayes and classical estimation of environmental factors for thebinomial distribution

An environmental factor converts reliability test results at one environmental condition into equivalent “failure” information at other environments. This paper studies environmental-factor estimation for the binomial distribution. Under general conditions, Bayes point estimates and credibility limits for environmental factors are derived. Classical point and confidence interval estimates are introduced and compared with the Bayes estimators. The characteristics of Bayes and classical estimators for the binomial distribution are summarized through numerical computation and theoretical analysis. A numerical example of reliability assessment by means of environmental factors is presented     

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.     

Estimation in the 2-parameter exponential distribution with priorinformation

This paper proposes a class of estimators for the scale parameter and for the mean of a 2-parameter exponential distribution, which is important in life testing and reliability theory, given a prior estimate of the scale parameter. The class of estimators for the scale parameter is motivated by the work of Jani (1991). These estimators have smaller mean square error than the classical estimators for all values of the location parameter, and for values of the scale parameter in a neighborhood of the prior estimate. Numerical computations indicate that certain of these estimators substantially improve the classical estimators for values of the scale parameter near the prior estimate, especially for small sample sizes     

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     

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.     

可靠性评估中利用先验信息的一种新思路

Bayes分析的一般程序是先验分布 数据=后验分布.提出一种新的Bayes分析程序:先基于数据诱导出参数的置信分布,而将参数的原先验分布等效成某种类型数据;然后将置信分布作为参数的"先验分布",而将先验信息的等效数据作为"试验数据";最后采用通常的Bayes方法进行可靠性评估.该新思路主要用于多参数寿命分布的可靠性评估.它克服了多参数模型下确定先验分布的困难及计算上的困难.仿真结果表明该方法具有较高的精度,且计算简单,可以应用于实际.     

Bayesian Estimation of Parameters of Life Distributions and Reliability from Type II Censored Samples

The Bayesian approach to reliability estimation from Type II censored samples is discussed here with emphasis on obtaining natural conjugate prior distributions. The underlying sampling distribution from which the censored samples are drawn follows a generalized life model (GLM) which includes a model proposed by Epstein and Sobel, Weibull, exponential, and Rayleigh distributions as special cases. Results are given for the Type II asymptotic distribution of largest values, Pareto, and Limited distribution. The natural conjugate prior, Bayes estimate for the generalized scale parameter, posterior risk, Bayes risk and Bayes estimate of the reliability function were derived for the distributions studied. In every case the natural conjugate prior is a 2-parameter family which provides a wide range of possible prior knowledge. Conjugate diffuse priors were derived. A diffuse prior, also called a quasi-pdf, is not a pdf because its integral is not unity. It represents roughly an informationless prior state of knowledge. The proper choice of the parameter for the diffuse prior leads to maximum likelihood, classical uniform minimum-variance unbiased estimator, and an admissible biased estimator with minimum mean square error as the generalized Bayes estimate. A feature of the GLM is the increasing function g(·) with possible applications in accelerated testing. KG(·) is a s-complete s-sufficient statistic for ?, and KG(·)/m is a maximum likelihood estimate for ?. Similar results were obtained for the Pareto, Type II asymptotic distribution of extremes, Pareto (associated with Pearl-Reed growth distribution) and others.     

Estimation of reliability of stress-strength model with non-identical component strengths

An attempt has been made in this paper to estimate the reliability of an s-out-of-k system with non-identical component strengths when component strengths follow an exponential distribution. A further assumption is made that all the components are subjected to a common random stress which also follow an exponential distribution. Bayes and maximum likelihood estimators of such system reliability are considered. A Bayes estimate is obtained by using Lindley's approximation. Comparisons are made on the basis of efficiency and Pitman nearness probability through a Monte-Carlo study.     

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     

Point and Interval Estimation, From One-Order Statistic, of the Location Parameter of an Extreme-Value Distribution with Known Scale Parameter and of the Scale Parameter of a Weibull Distribution with Known Shape Parameter

This paper derives a one-order statistic estimator ?mn b for the location parameter of the (first) extreme-value distribution of smallest values with cumulative distribution function F(x;u,b) = 1 - exp {-exp[(x-u)/b]} using the minimum-variance unbiased one-order statistic estimator for the scale parameter of an exponential distribution, as was done in an earlier paper for the scale parameter of a Weibull distribution. It is shown that exact confidence bounds, based on one-order statistic, can be easily derived for the location parameter of the extreme-value distribution and for the scale parameter of the Weibull distribution, using exact confidence bounds for the scale parameter of the exponential distribution. The estimator for u is shown to be b ln cmn + xmn, where xmn is the mth order statistic from an ordered sample of size n from the extreme-value distribution with scale parameter b and Cmn is the coefficient for a one-order statistic estimator of the scale parameter of an exponential distribution. Values of the factor cmn, which have previously viously been tabulated for n = 1(1)20, are given for n = 21(1)40. The ratios of the mean-square-errors of the maximum-likelihood estimators based on m order statistics to those of the one-order statistic estimators for the location parameter of the extreme-value distribution and the scale parameter of the Weibull distribution are investigated by Monte Carlo methods. The use of the table and related tables is discussed and illustrated by numerical examples.