两个Weibull分布尺度参数比的推断

本文研究两个Weibull分布尺度参数比的推断.利用广义枢轴量和广义检验变量分别给出尺度参数比的广义置信区间和假设检验.证明了在形状参数相等时由广义枢轴量确定的尺度参数比的100(1-α)%广义置信区间的覆盖概率为1-α(0＜α＜1).由广义p-值确定的固定水平检验具有真实水平.讨论了形状参数不等时尺度参数比的推断,给出频率性质,通过与前人的结果模拟比较得出本文的方法能更好地解决尺度参数比的推断问题.最后研究两个Weibull分布形状参数比的假设检验,证明由广义p-值确定的固定水平检验具有真实水平.     

一类分布未来观测值的预测

利用贝叶斯方法对一类分布的未来观测值作出预测,此类分布是包括了Weibull,复合Weibull(三参数Burr-type X II),pareto,beta等分布的广义分布,先验分布也给出一个一般形式,其因参数的不同适合不同的分布.本文在所得历史样本为type II双删失情况下,获得了在单、双样本场合未来观测值的预测分布,并举例加以说明.     

两个广义伽玛分布之间新的Kullback-Leibler距离

在Kullback-Leibler距离的基础上,对Kullback-Leibler距离进行改进,给出了新的Kullback-Leibler距离,并讨论了它的性质.计算了两个不同广义伽玛分布之间新的Kullback-Leibler距离.推导出伽玛分布、Weibull分布、Rayleigh分布、正态分布、指数分布新的Kullback-Leibler距离.另外在新的KullbackLeibler距离下,还得到digamma函数Ψ(x)=(Γ'(x)/(Γ(x))为单调递增函数.     

指数和Weibull串联系统环境因子的统计推断

给出了Weibull串联系统环境因子的定义,且研究了元件寿命服从指数分布和Weibull分布时串联系统环境因子的点估计和区间估计,并利用模拟方法研究了所给点估计的精度和广义置信区间的覆盖率.模拟结果表明所给方法是令人满意的.     

Weibull分布下基于遗传算法的设备寿命预测

本文研究了Weibull分布下设备可靠性寿命预测的问题.利用改进后的遗传算法,主要是对遗传算法编码、目标函数和遗传操作的改进,实现对Weibull参数的估计.获得了Weibull分布模型和设备的可靠性寿命模型.     

完全数据下Weibull分布参数的极大似然估计

在完全数据条件下对Weibull分布,分别使用Newton-Raphson算法、CM算法及修正的CM算法进行完全数据Weibull分布参数的极大似然估计计算,并且在得到相应的迭代公式后,进行随机模拟.从模拟结果来分析这三种算法在处理Weibull分布参数的极大似然估计的优良性.     

Weibull分布场合无失效数据的可靠性验证试验

对于Weibull分布的无失效数据问题,利用Bayes方法给出了产品寿命服从Weibull分布,形状参数的先验分布为U(0,1),尺度参数为1,假定产品的可靠性指标达到某个给定的值的情况下,无失效数据的可靠性验证试验,并利用相同的分析方法给出形状参数的Bayes估计.     

不同阶段变环境下Weibull分布的可靠性估计

本文研究了Weibull分布在不同阶段和环境下的设备可靠性,给出了Weibull分布不同阶段和环境下的参数估计方法,并证明了估计量的一致强相合性,模拟结果表明方法的实践可用性。     

Weibull分布N〉25时的BLUE系数

本文导出了标准极值分布O.S.的矩(期望、方差、协方差)的简单计算公式,由此计算出了Weibull分布样本容量n>25时的BLUE系数。     

分组型数据三参数Weibull分布的参数估计

给出了在分组型数据下三参数Weibull分布的参数估计,并通过Monte-Carlo模拟说明了本文方法的可行性.     

TFR模型序加试验下WEIBULL分布产品寿命的统计分析

本文针对损伤效率(TFR)模型,首次提出将步加试验推广至序加试验,给出了两参数Weibull分布参数的极大似然估计.     

TFR、TRV和CE模型序加试验下WEIBULL分布产品的失效分布

本针对TFR模型，首次提出将步加试验推广至序加试验，就两参数Weibull分布给出了损伤因子函数，同时给出了产品寿命的残存函数，另外针对TRV模型，在序加试验下就两参数Weibull分布给出了损伤系数，同时给出了产品寿命的残存函数。     

Compound negative binomial shared frailty models for bivariate survival data

In this paper, we introduce a new shared frailty model called the compound negative binomial shared frailty model with three different baseline distributions namely, Weibull, generalized exponential and exponential power distribution. To estimate the parameters involved in these models we adopt Markov Chain Monte Carlo (MCMC) approach. Also we apply these three models to a real life bivariate survival data set of McGrilchrist and Aisbett (1991) related to kidney infection and suggest a better model for the data.     

Exponentiated generalized linear exponential distribution

A new generalization of the linear exponential distribution is recently proposed by Mahmoud and Alam [1], called as the generalized linear exponential distribution. Another generalization of the linear exponential was introduced by Sarhan and Kundu  and , named as the generalized linear failure rate distribution. This paper proposes a more generalization of the linear exponential distribution which generalizes the two. We refer to this new generalization as the exponentiated generalized linear exponential distribution. The new distribution is important since it contains as special sub-models some widely well known distributions in addition to the above two models, such as the exponentiated Weibull distribution among many others. It also provides more flexibility to analyze complex real data sets. We study some statistical properties for the new distribution. We discuss maximum likelihood estimation of the distribution parameters. Three real data sets are analyzed using the new distribution, which show that the exponentiated generalized linear exponential distribution can be used quite effectively in analyzing real lifetime data.     

Comparisons of models for estimation of safe doses using measures of the heaviness of tail of a distribution

Summary Procedures to estimate a dose of which the incidence probability is very small (e.g. 10⁻⁶) have been developed to evaluate the safety of chemical compounds. To compare models for estimation of safe doses quantitatively, a measure of the heaviness of tail of a distribution and a measure of tail at the origin are introduced. These measures have a theoretical basis for the comparison of tail behavior between distributions. Using the two measures, a tail ordering is defined to present a criterion for the comparison of models and is discussed for the probit, the logit, the Weibull, the (generalized) multihit, the (generalized) multitarget and the multistage models. The multistage model is most conservative among them, while the probit model has the reverse property. The Weibull model is more conservative than the logit. The multihit and multitarget models are found to be more sensitive than the Weibull and the logit. The Institute of Statistical Mathematics National Institute of Environmental Health Sciences     

Marshall and Olkin’s Distributions

A review is provided of the continuous and discrete distributions introduced by the eminent Professors Marshall and Olkin. The topics reviewed include: bivariate geometric distribution, extreme value behavior, bivariate negative binomial distribution, bivariate exponential distribution, concomitants, reliability, distributions of sums and ratios, Ryu’s bivariate exponential distribution, bivariate Pareto distribution and generalized exponential and Weibull distributions. Some hitherto unknown results about these distributions are also mentioned. This is a tribute to the work of Professors Marshall and Olkin.     

On Rates of Uniform Convergence of Lower Extreme Generalized Order Statistics

Under a von Mises-type condition the joint distribution of suitable normalized lower extreme generalized order statistics converges w.r.t. the variational distance to the asymptotic joint distribution of lower extreme order statistics. Rates of uniform convergence are established. It turns out that the rates of uniform convergence known for ordinary extremes carry over to lower generalized extremes. Finally, models of Weibull type are concerned, where uniform rates are used in connection with model approximations in order to simplify statistical inference.AMS 2000 Subject Classification. Primary—60G70     

极值分布和威布尔分布异常数据的检验方法

本文对威布尔分布的极值分布异常数据的检验给出了一系列的方法，首先，导入了极值分布下一般Ｄｉｘｏｎ型统计量的精确分布，同时还给出了改进的Ｇ型统计量，及它们的分位点表。最后本文提出了一个新的统计量；Ｆ型统计量，并用Ｍｏｎｔｅ－Ｃａｒｌｏ模拟的方法给出其分位点表，从而首次给出威布尔分布异常值的直接检验方法。本文进一步讨论了这些检验方法的功效，且表明Ｆ型检验是最优的。