Inference on Weibull Percentiles and Shape Parameter from Maximum Likelihood Estimates

Four functions of the maximum likelihood estimates of the Weibull shape parameter and any Weibull percentile are found. The sampling distributions are independent of the population parameters and depend only upon sample size and the degree of (Type II) censoring. These distributions, once determined by Monte Carlo methods, permit the testing of the following hypotheses: 1) that the Weibull shape parameter is equal to a specified value; 2) that a Weibull percentile is equal to a specified value; 3) that the shape parameters of two Weibull populations are equal; and 4) that a specified percentile of two Weibull populations are equal given that the shape parameters are. The OC curves of the various tests are shown to be readily computed. A by-product of the determination of the distribution of the four functions are the factors required for median unbiased estimation of 1) the Weibull shape parameter, 2) a Weibull percentile, 3) the ratio of shape parameters of two Weibull distributions, and 4) the ratio of a specified percentile of two Weibull distributions.     

Selecting, under type-II censoring, Weibull populations that aremore reliable

It is pointed out that the problem of selecting Weibull populations that are more reliable is complex; the main result is that there is no simple selection rule. Under type-II censoring, the use of a locally optimal selection rule when the shape parameters are known and the use of a modified selection rule when the unknown shape parameters have some prior distributions are proposed. The performance of this modified rule was tested extensively by simulation; this rule was shown to be quite robust for a variety of beta prior distributions     

One-Order-Statistic Estimation of the Scale Parameters of Weibull Populations

This paper calculates the minimum-variance unbiased one-order-statistic estimator of the parameter of a one-parameter exponential population. The estimator is given for N = 2(1)20 along with its efficiency with respect to an unbiased M-order-statistic estimator for a sample of N items which is truncated after M items have failed. Furthermore, it is shown that by using the estimator for exponential populations one can obtain a consistent estimator for the scale parameter of Weibull populations with any known shape parameter and with ## location parameter zero. A section on the use of the tabled data and a numerical example are included.     

Multiple Comparison for Weibull Parameters

Two problems are considered: 1) testing the hypothesis that the shape parameters of k 2-parameter Weibull populations are equal, given a sample of n observations censored (Type II) at r failures, from each population; and 2) Under the assumption of equal shape parameters, the problem of testing the equality of the p-th percentiles. Test statistics (for these hypotheses), which are simple functions of the maximum likelihood estimates, follow distributions that depend only upon r,n,k,p and not upon the Weibull parameters. Critical values of the test statistics found by Monte Carlo sampling are given for selected values of r,n,k,p. An expression is found and evaluated numerically for the exact distribution of the ratio of the largest to smallest maximum likelihood estimates of the Weibull shape parameter in k samples of size n, Type II censored at r = 2. The asymptotic behavior of this distribution for large n is also found.     

Reliability tests for Weibull distribution with varyingshape-parameter, based on complete data

The Weibull distribution indexed by scale and shape parameters is generally used as a distribution of lifetime. In determining whether or not a production lot is accepted, one wants the most effective sample size and the acceptance criterion for the specified producer and consumer risks. (μ₀ ≡ acceptable MTTF; μ₁ ≡ rejectable MTTF). Decide on the most effective reliability test satisfying both constraints: Pr{reject a lot | MTTF = μ₀} ⩽ α, Pr{accept a lot | MTTF = μ₁ } ⩽ β. α, β are the specified producer, consumer risks. Most reliability tests for assuring MTTF in the Weibull distribution assume that the shape parameter is a known constant. Thus such a reliability test for assuring MTTF in Weibull distribution is concerned only with the scale parameter. However, this paper assumes that there can be a difference between the shape parameter in the acceptable distribution and in the rejectable distribution, and that both the shape parameters are respectively specified as interval estimates. This paper proposes a procedure for designing the most effective reliability test, considering the specified producer and consumer risks for assuring MTTF when the shape parameters do not necessarily coincide with the acceptable distribution and the rejectable distribution, and are specified with the range. This paper assumes that α < 0.5 and β < 0.5. This paper confirms that the procedure for designing the reliability test proposed here applies is practical     

Asymptotic sampling distribution of inversecoefficient-of-variation and its applications

This paper develops the asymptotic sampling distribution of the inverse of the coefficient of variation (InvCV). This distribution is used for making statistical inference about the population CV (coefficient of variation) or InvCV without making an assumption about the population distribution. It applies to making inferences (point and interval estimation, and hypothesis-testing) about the shape parameter of some popular lifetime distributions like the Gamma, Weibull, and log-normal, when the scale parameter is unknown. The test procedure is used to test exponentiality against a Gamma or a Weibull alternative. The results are compared with those in the literature     

Simultaneous Estimation of Location and Scale Parameters of the Weibull Distribution

This paper deals with the simultaneous estimation of the location parameter ? and the scale parameter ? of the Weibull distribution when both are unknown and the shape parameter ? is known. The best linear unbiased estimate (BLUE) (?, ?) based on a subset of k optimum ordered observations selected from the whole sample is compared with 1) Ogawa's asymptotically best linear estimate (ABLE) (?*, ?*) based on k ordered observations whose ranks are approximated by an asymptotic optimum selection, and 2) the BLUE based on the ranks in 1). Tables facilitating the computation of (?, ?) based on k = 3, 4 optimum ordered observations are provided.     

基于LSSVM的威布尔分布形状参数估计

固态介质击穿寿命特性通常用威布尔分布来描述,形状参数卢反应了固态介质的失效特征,因而需要精确估计β值.提出了在小样本情况下基于最小二乘支持向量机(LSSVM)的参数评估方法,并给出了LSSVM在MOS电容与时间有关的击穿寿命分布评估中的应用实例,并与常规的最小二乘评估方法相比,得到的结果表明LSSVM的评估精度更高(均方误差更小)、鲁棒性更好,在小样本情况下能更精确地确定威布尔分布的形状参数.     

Complete Sample Estimation Techniques for Reparameterized Weibull Distributions

The Weibull distribution, frequently employed to assign probabilities to the lifetimes of components and systems operating under stress, is habitually characterized by a pair of positive parameters, termed the scale and shape parameters. Two fundamental reparameterizations of the Weibull probability density function are proposed. The first reparameterization replaces the shape parameter by its inverse, the resulting positive parameter thereafter termed the shaping parameter. This permits a more facile exposition of the properties of parameter estimates, derived in the event that a complete random sample from the Weibull distribution is available. The characteristics of these parameter estimation techniques are then reviewed and compared, and their variances and distributional properties are delineated whenever possible. A second reparameterization extends the parameter space so as to include nonpositive values of the shape parameter. This extension augments the utility and applicability of the Weibull distribution without requiring radical alteration of the standard parameter estimation procedures applicable to the original parameter space.     

An Evaluation of Exponential and Weibull Test Plans

MIL-STD-781B gives sampling plans (sequential and fixed length) for reliability tests under the assumption of a constant failure rate. Using Monte Carlo techniques, the authors compare s-expected time to a decision and producer and consumer risks for some of these plans. It is shown that plans which assume an exponential distribution are not robust to departures from that assumption. A simple modification of these plans for use when life has a Weibull distribution with known shape parameter not equal to one, and an adaptive test procedure for use when life has a Weibull distribution with unknown shape parameter are proposed. The modified plans for a Weibull distribution with known shape parameter have the same designated producer and consumer risks, but different s-expected time to a decision than the corresponding exponential plans. Using Monte Carlo techniques, the authors determine s-expected time to a decision and producer and consumer risks for various forms of the adaptive procedure.     

Estimating Weibull Parameters for a General Class of Devices from Limited Failure Data

Relatively simple approaches to estimating Weibull parameters for a general class of devices are developed through regression models. It is assumed that data are collected on a number of device types belonging to a general class. For each device type, the only information available is the number of devices being observed, the total time observed and the total number of failures. By assuming a constant shape parameter and a scale parameter that may vary with the characteristics of the device-type, the least squares method is used to provide estimates of the parameters of a two-parameter Weibull distribution for both replacement and nonreplacement data. An approach is also suggested for dealing with troublesome cases of zero failure occurrences. A numerical example is provided to illustrate the approach.     

Robustness of the exponential sequential probability ratio test (SPRT) when Weibull distributed failures are transformed using a “known” shape parameter

Assumptions accompanying exponential failure models are often not met in the life-testing of many products. Several authors have suggested sequential life testing techniques that transform Weibull failure times to an exponential density using the “known” Weibull shape parameter. In practice, this parameter is never known and must be estimated. This paper demonstrates that procedures based on this transformation are extremely sensitive to mis-specification of the shape parameter. Furthermore, it is doubtful that the shape parameter may be estimated with enough precision to successfully implement these procedures. Using a Weibull sequential test without the transformation yields better results; however, sensitivity analysis to shape parameter mis-specification is recommended before any specific test is implemented.     

Threshold optimization of decentralized CFAR detection in weibull clutter using genetic algorithms

The use of genetic algorithms (GAs) tool for the solution of distributed constant false alarm rate (CFAR) detection for Weibull clutter statistics is considered. An approximate expression of the probability of detection (P _D) of the ordered statistics CFAR (OS-CFAR) detector in Weibull clutter is derived. Optimal threshold values of distributed maximum likelihood CFAR (ML-CFAR) detectors and distributed OS-CFAR detectors with a known shape parameter of the background statistics are obtained using GA tool. For the distributed ML-CFAR detection, we consider also the case when the shape parameter is unknown of the Weibull distribution. A performance assessment is carried out, and the results are compared and given as a function of the shape parameter and of system parameters.     

Bayes interval for the Weibull parameters utilizing guessed estimates

In this paper an attempt is made to provide a method of obtaining the HPD-intervals for the scale and shape parameters of the Weibull distribution, when a prior distribution of the parameter places a weight (1 − b) on the guess value of the parameter and distributes the rest probability mass b according to some specified distribution. The equal tail credible intervals have been obtained and it is proposed that these limits be used as initial points for obtaining the HPD-intervals.     

On estimating the shape parameter of the Weibull distribution by shrinkage towards an interval

This paper proposes some shrunken estimators for the shape parameter of the Weibull distribution under censored sampling when some apriori or guessed interval containing the parameter β is available. The extensions of the work done in Pandey and Singh (1984) have been considered. Comparisons of the proposed estimators with the usual unbiased estimator, in terms of mean squared error are made. It is found that the proposed estimators are preferable to the usual estimator in some guessed interval of the parameter space of β.     

An alternative degradation reliability modeling approach using maximum likelihood estimation

An alternative degradation reliability modeling approach is presented in this paper. This approach extends the graphical approach used by several authors by considering the natural ordering of performance degradation data using a truncated Weibull distribution. Maximum Likelihood Estimation is used to provide a one-step method to estimate the model's parameters. A closed form expression of the likelihood function is derived for a two-parameter truncated Weibull distribution with time-independent shape parameter. A semi-numerical method is presented for the truncated Weibull distribution with a time-dependent shape parameter. Numerical studies of generated data suggest that the proposed approach provides reasonable estimates even for small sample sizes. The analysis of fatigue data shows that the proposed approach yields a good match of the crack length mean value curve obtained using the path curve approach and better results than those obtained using the graphical approach.     

An approximation for largeconsecutive-k-out-of-n:F systems

The authors consider a consecutive-k-out-n:F system consisting of identically distributed and statistically independent components, where the life distribution of an individual component is Weibull distributed with scale parameter 1/λ and shape parameter B. Let T_n be the life length of the consecutive-k-out-of-n:F system. The authors prove that for large values of n, the distribution of the n ¹(ka)/T_n, is satisfactorily approximated by a Weibull distribution with the same scale parameter and shape parameter k times the original shape parameter     

Statistical model of an undermoded reverberation chamber

Weibull distribution is adopted to model the electric field component of a Reverberation Chamber (RC). Its first property is to include the asymptotic laws, such as Rayleigh and exponential, and its main advantage lies in the fact that the Weibull shape parameter enables a model of the departure from overmoded to undermoded RC regime. Applications are given, such as an RC modal finite element modeling and a Monte Carlo simulation: they prove that the Weibull two-parameter distribution correctly models the quality factor influence. Moreover, the relevance of the use of this extreme value distribution is illustrated.     

The effect of Weibull fading channel on cooperative spectrum sensing network using an improved energy detector

This paper analyses the performance of proposed cooperative spectrum sensing (CSS) network in Weibull fading environment. First, we have derived the novel analytic expressions for probabilities of missed detection and false alarm in Weibull fading channel, assuming an improved energy detector (IED), selection combining diversity scheme and multiple antennas at each cognitive radio (CRs). Next, performance is analyzed using complementary receiver operating characteristics curves, total error rate, average channel throughput, and network utility function curves for the proposed CSS network. The optimal performance of CSS network is achieved by optimizing the CSS network parameters. The closed form of expressions for the optimum value of number of CRs, arbitrary power of received signal, and detection threshold at each CR are derived using OR-Rule and AND-Rule at fusion center (FC). The average channel throughput and network utility function analysis are evaluated using \(k=1+n\) and \(k=N-n\) fusion rules at FC. Finally, the impact of several network parameters such as, multiple antennas at each CR (M), number of CRs (N) in CSS network, Weibull fading parameter (V), arbitrary power of received signal (p), and sensing channel SNR (\({\bar{\gamma }})\) on the performance of proposed CSS network are investigated using the simulation results. The performance comparison between conventional energy detector and an IED has been highlighted with the simulations.