混合截尾和定时截尾试验weibull分布抽样方案的比较

混合截尾试验是定时和定数截尾的一种有用的推广。本文研究了Ｗｅｉｂｕｌｌ分布和混合截尾试验的一次抽样方案,并对可靠决策损失函数给出了贝叶斯风险的显式表达式。比较陈和林的模型（１９９９）,我们得混合截尾试验的抽样方案于定时抽样方案。     

截尾寿命试验中参数最大似然估计的重对数律

本文对于包含定数和定时截尾寿命试验的混合型寿命试验，研究了分布参数的最大似然估计．基于截尾数据，证明了最大似然估计的收敛速度符合重对数律．     

混合截尾似然比的局部渐近正态性及应用

本文对于一类包含定数和定时截尾寿命试验的混合型寿命试验,建立了基于截尾数据的似然比的局部渐近正态性。由此我们得到了基于截尾数据的MLE和Bayes估计的渐近性质。     

指数分布定时截尾寿命试验失效率的近似置信区间

本文研究了指数分布大样本定时截尾寿命试验，给出了总试验时间的极限分布；并给出了失效率的近似置信区间。     

截尾分布与截尾样本的统计分析

[1]中给出了样本容量n已知的(n,r）定时截尾不完全样本的分析方法。在[2]中我们讨论了n未知的（n,r）定时截尾不完全样本并较好地处理了晚截尾的接近完全样本的分析。本文中我们相应于截尾样本，引入截尾分布，通过用截尾样本拟合截尾分布而得到寿命T的整体分布。此方法无论对n已知还是n未知，对早截尾还是晚截尾的（n,r）定时截尾样本都适用。并且此方法还可推广到对其它形式的不完全样本的处理。     

关于寿命试验的某些统计分析——(指数分布的情形)(Ⅳ)

<正> §3.4. 有替换的(右边)定时截尾寿命试验的情形由于在有替换的定时截尾寿命试验中,被观察的失效个数 K 是一个随机变量,其概率模型为     

定时与定数截尾试验参模的建立与参数估计

对寿命试验建立起定时与定数截尾参数模型.考虑定时截尾产品失效数的随机性引入修偏项,应用随机跟随修偏估计方法;对定数截尾考虑数据信息的不完全性,按抽样多少探讨出最佳线性与简单线性无偏估计方法,并给出数据分析结果.     

基于广义逆理论的步进应力加速寿命试验的最优设计

步进应力加速寿命试验的最优设计问题早在20世纪60年代就已提出.本文在应力个数和未知参数个数不相等的情况下,以MLE渐近方差最小为准则,运用广义加号逆的理论,解决指数分布时定数截尾寿命试验和定时截尾寿命试验的最优设计问题.通过理论推导,得出定数截尾寿命试验时的不同应力下失效产品数的安排,以及定时截尾寿命试验时的最优应力变换点.并且,对每一种情况进行了数据模拟,结果显示,这些理论和方法在实际运用中是可行的和有效的.     

指数寿命定时截尾数据情形下可靠度的置信限

本文提供了一种指数寿命定时截尾寿命试验数据情形下参数的点估计方法,并在该点估计的基础上,利用Winterbottom[1]推广了的Cornish－Fisher（记为WCF）展开给出了参数的区间估计．进一步讨论了部件为指数寿命定时截尾寿命试验数据情形下系统可靠性综合问题,并通过数值模拟说明本文方法的可行性．     

总定数先定时截尾情况下简单步进应力加速寿命试验的优化设计

讨论了总定数先定时截尾情况下简单步进应力加速寿命试验的最优设计问题,然后同定数截尾情况下简单频进应力加速寿命试验的最优设计进行比较,比较结果表明本文方法较优,文中的例子进一步验证了上述结论。     

Estimating parameters in one-way analysis of covariance model with short-tailed symmetric error distributions

We consider one-way analysis of covariance (ANCOVA) model with a single covariate when the distribution of error terms are short-tailed symmetric. The maximum likelihood (ML) estimators of the parameters are intractable. We, therefore, employ a simple method known as modified maximum likelihood (MML) to derive the estimators of the model parameters. The method is based on linearization of the intractable terms in likelihood equations. Incorporating these linearizations in the maximum likelihood, we get the modified likelihood equations. Then the MML estimators which are the solutions of these modified equations are obtained. Computer simulations were performed to investigate the efficiencies of the proposed estimators. The simulation results show that the proposed estimators are remarkably efficient compared with the conventional least squares (LS) estimators.     

Reliability inference of stress–strength model for the truncated proportional hazard rate distribution under progressively Type-II censored samples

This paper considers the reliability inference for the truncated proportional hazard rate stress–strength model based on progressively Type-II censoring scheme. When the stress and strength variables follow the truncated proportional hazard rate distributions, the maximum likelihood estimation and the pivotal quantity estimation of stress–strength reliability are derived. Based on the percentile bootstrap sampling technique, the 95% confidence interval of stress–strength reliability is obtained, as well as the related coverage percentage. Moreover, based on the Fisher Z transformation and the modified generalized pivotal quantity, the 95% modified generalized confidence interval for the stress–strength reliability is obtained. The performance of the proposed method is evaluated by the Monte Carlo simulation. The numerical results show that the pivotal quantity estimators performs better than the maximum likelihood estimators. At last, two real datasets are analyzed by the proposed methodology for illustrative purpose. The results of real example analysis show that our model can be applied to the practical problem, the truncated proportional hazard rate distribution can fit the failure data better than other distributions, and the algorithms in this paper are suitable to handle the small sample data.     

Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method

We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work out modified maximum likelihood equations by expressing the maximum likelihood equations in terms of ordered residuals and linearizing intractable nonlinear functions (Tiku and Suresh, 1992) [8]. The solutions, called modified maximum estimators, are explicit functions of sample observations and therefore easy to compute. They are under some very general regularity conditions asymptotically unbiased and efficient (Vaughan and Tiku, 2000) [4]. We show that for small sample sizes, they have negligible bias and are considerably more efficient than the traditional least squares estimators. We show that our estimators are robust to plausible deviations from an assumed distribution and are therefore enormously advantageous as compared to the least squares estimators. We give a real life example.     

Bivariate Birnbaum–Saunders distribution and associated inference

Univariate Birnbaum–Saunders distribution has been used quite effectively to model positively skewed data, especially lifetime data and crack growth data. In this paper, we introduce bivariate Birnbaum–Saunders distribution which is an absolutely continuous distribution whose marginals are univariate Birnbaum–Saunders distributions. Different properties of this bivariate Birnbaum–Saunders distribution are then discussed. This new family has five unknown parameters and it is shown that the maximum likelihood estimators can be obtained by solving two non-linear equations. We also propose simple modified moment estimators for the unknown parameters which are explicit and can therefore be used effectively as an initial guess for the computation of the maximum likelihood estimators. We then present the asymptotic distributions of the maximum likelihood estimators and use them to construct confidence intervals for the parameters. We also discuss likelihood ratio tests for some hypotheses of interest. Monte Carlo simulations are then carried out to examine the performance of the proposed estimators. Finally, a numerical data analysis is performed in order to illustrate all the methods of inference discussed here.     

Optimizing in the class of Fuller modified limited information maximum likelihood estimators

A general class of Fuller modified maximum likelihood estimators are considered. It is shown that this class possesses finite moments. Asymptotic bias and asymptotic mean squared error are derived using small-σ expansions. A simulation study is carried out to compare different estimators in this class with standard estimators.     

Parameter estimation in geometric process with Weibull distribution

We consider geometric process (GP) when the distribution of the first occurrence time of an event is assumed to be Weibull. Explicit estimators of the parameters in GP are derived by using the method of modified maximum likelihood (MML) proposed by Tiku [24]. Asymptotic distributions and consistency properties of these estimators are obtained. We show that our estimators are more efficient than the widely used modified moment (MM) estimators via Monte Carlo simulation study. Further, two real life examples are given at the end of the paper.     

Maximum likelihood estimation of covariance matrices under simple tree ordering

The closed-form maximum likelihood estimators for the completely balanced multivariate one-way random effect model are obtained by Anderson et al. (Ann. Statist. 14 (1986) 405). It remains open whether there exist the closed-form maximum likelihood estimators for the more general completely balanced multivariate multi-way random effects models. In this paper, a new parameterization technique for covariance matrices is used to grasp the inside structure of likelihood function so that the maximum likelihood equations can be dramatically simplified. As such we obtain the closed-form maximum likelihood estimators of covariance matrices for Wishart density functions over the simple tree ordering set, which can then be applied to get the maximum likelihood estimators for the completely balanced multivariate multi-way random effects models without interactions.     

Stress–strength reliability estimation under the standard two-sided power distribution

In this report, the distribution for setting up a system reliability exposed to some stress is studied. The standard two-sided power distribution is assumed to be the underlying distribution. We obtained the exact expressions and estimates for the reliability by applying different methods such as maximum likelihood and Bayesian estimators. Three different scenarios were examined: known and equal reflection parameters, known but unequal reflection parameters, and all parameters are unknown, providing practical guidance and recommendations for the estimator design. For large samples, we recommend use of the parametric bootstrap method with the maximum likelihood estimate. Real data sets were used to illustrate the performances of the estimators.     

Lomax分布极大似然估计的两点研究

本文研究了Lomax分布参数极大似然估计的存在性和估计量的收敛性问题.利用严格的分析法和中心极限定理,获得了Lomax分布极大似然估计的存在性和估计量的渐近正态分布的结果,进一步推广到了有缺失数据的两个Lomax总体中,参数的极大似然估计有强相合性和渐近正态性.     

Parametrization Invariance with Respect to Second Order Admissibility Under Mean Squared Error

Our main concern is about second order admissibility under mean squared error. A sufficient condition and a necessary condition for a modified maximum likelihood estimator to be second order admissible regardless of parametrization are obtained. In addition, some procedures for characterizing such estimators are provided.