一个新的具有单调降失效率的寿命分布

本文由Pareto分布和Logarithmic分布"混合"生成两参数具有单调降失效率的新型寿命分布,研究了该分布的矩、熵、失效率函数、平均剩余寿命和参数的极大似然估计,应用EM算法求参数的极大似然估计,进行了数值模拟.     

Pareto-Geometric分布

本文提出了一种具有单调失效率的新型寿命分布, 即由Pareto分布和Geometric分布生成的两参数的Pareto-Geometric分布, 研究了该分布的各种性质和参数极大似然估计的存在唯一性, 并应用 EM 算法得到了参数的极大似然估计值和相应的渐近方差、协方差.     

Alpha Power广义指数分布

研究了一种新的寿命分布,称为Alpha power广义指数分布.新分布的危险率函数具有单调、浴盆和单峰形状,更加灵活地适用于可靠性分析中寿命数据拟合.讨论了新分布的统计特征,包括分位数、矩和矩母函数、熵、次序统计量,并讨论了参数的最大似然估计.通过两组真实寿命数据拟合分析说明了新分布的优越性.     

几何分布产品简单步进应力加速寿命试验TFR模型下的统计分析

首次将损伤失效率模型应用于离散型寿命分布场合,给出了TFR模型几何分布产品简单步加试验下参数的极大似然估计.     

单参数指数分布产品截尾样本场合简单步进应力加速寿命试验损伤失效率模型下的统计分析

本文给出单参数指数分布产品截尾样本场合简单步进应力加速寿命试验损伤失效率模型下参数的极大似然估计和拟矩估计。     

几何分布产品全样本场合步进应力加速寿命试验TFR模型下的统计分析

讨论了几何分布产品在步进应力加速试验TFR模型下寿命分布.给出了其寿命分布函数步进形式,在全样本场合利用极大似然估计方法和矩估计方法求出了未知参数的点估计,最后利用计算机模拟说明本文方法的可行性.     

WEIBULL分布步进应力加速寿命试验损伤失效率模型参数的近似极大似然估计和逆矩估计

Bhattacharyya和Soejoeti(1980)对步进应力加速寿命试验提出损伤失效率模型(TFR模型)．本针对TFR模型，对两参数Weibull分布，在步进应力加速试验下给出了参数的近似极大似然估计和逆矩估计，并通过Montr-Carlo模拟考察了估计的精度，比较了各估计的优劣。     

两参数指数分布产品全样本场合下步进应力加速寿命试验损伤失效率模型下的统计分析

本文给出了两参数指数分布产品全样本场合下步进应力加速寿命试验损伤失效率模型下参数的极大似然估计和拟矩估计,并通过Monte-Carlo模拟说明本文方法的可行性.     

几何分布截尾样本场合步进应力加速寿命试验TFR模型下的统计分析

讨论了几何分布产品在步进应力加速试验TFR模型下寿命分布.给出了其寿命分布函数步进形式,在截尾样本场合利用极大似然估计方法和拟矩估计方法求出了未知参数的点估计,最后利用计算机模拟考察了说明本文方法的可行性.     

广义BS-极小值疲劳寿命分布的统计分析方法研究

论文提出一种新的疲劳寿命分布—两参数广义Birnbaum-Saunders极小值分布(BSMin(α,β)),研究了该分布的密度函数与失效率函数的图像特征。其次,给出了该分布在全样本下两个参数的分位数估计与回归估计,并通过蒙特卡罗模拟比较发现分位数估计较优,同时也探讨了两个参数的矩估计、极大似然估计以及对数矩估计。此外,论文还指出BSMin(α,β)分布取对数后用泰勒展开可近似看作两参数极小值分布,由此得到两个参数的近似区间估计,并通过蒙特卡罗模拟考察了近似区间估计的精度。最后,利用模拟数据说明了论文所提的点估计和近似区间估计方法的应用。     

Statistical Analysis of Series System for Masked Data under Successive Censored Life Test

In this paper, considering series system of masked data under simple successive censored and multiple successive censored life test, the likelihood function and maximum likelihood estimate are respectively proposed for series system composed of two units under two kinds of situations. One is the series system composed of two units with constant failure rate, and the other is the series system composed of two units with linear failure rate through the origin. The approximate interval estimates of parameters are given by using the method of likelihood ratio. Besides, the examples show the feasibility of the methods through Monte-Carlo simulations.     

Parameter estimation for a two-parameter bathtub-shaped lifetime distribution

A two-parameter distribution was revisited by Chen (2000) [7]. This distribution can have a bathtub-shaped or increasing failure rate function which enables it to fit real lifetime data sets. Maximum likelihood and Bayes estimates of the two unknown parameters are discussed in this paper. It is assumed in the Bayes case that the unknown parameters have gamma priors. Explicit forms of Bayes estimators cannot be obtained. Different approximations are used to establish point estimates and two sided Bayesian probability intervals for the parameters. Monte Carlo simulations are applied to the comparison between the maximum likelihood estimates and the approximate Bayes estimates obtained under non-informative prior assumptions. Analysis of a real data set is also been presented for illustrative purposes.     

逐步增加Ⅱ型截尾下Pareto分布的Bayes估计

基于逐步增加的Ⅱ型截尾样本,当Pareto分布的尺度参数已知时,分别在平方损失和LINEX损失下讨论了其形状参数和可靠性指标(失效率和可靠度)的Bayes估计,并用Monte-Carlo方法对估计结果的MSE,进行了模拟比较.结果表明了在LINEX损失下的估计结果更有效.     

The generalized Gompertz distribution

This paper deals with a new generalization of the exponential, Gompertz, and generalized exponential distributions. This distribution is called the generalized Gompertz distribution (GGD). The main advantage of this new distribution is that it has increasing or constant or decreasing or bathtub curve failure rate depending upon the shape parameter. This property makes GGD is very useful in survival analysis. Some statistical properties such as moments, mode, and quantiles are derived. The failure rate function is also derived. The maximum likelihood estimators of the parameters are derived using a simulations study. Real data set is used to determine whether the GGD is better than other well-known distributions in modeling lifetime data or not.     

Statistical analysis of progressive stress accelerated life test for the product of two-parameter Laplace BS fatigue life distribution under inverse power law model

Based on the product of two-parameter Laplace Birnbaum-Saunders fatigue life distribution, its failure distribution mode is theoretically derived under the progressive stress accelerated life test with inverse power law model, and then three-parameter generalized Laplace Birnbaum-Saunders fatigue life distribution is introduced. The basic properties of three-parameter generalized Laplace Birnbaum-Saunders fatigue life distribution are analyzed, and the image characteristics of its density function, failure rate function and average failure rate function are investigated. Meanwhile, the point estimate method is given for three parameters, and then the point estimates of parameters are obtained for the product of two-parameter Laplace Birnbaum-Saunders fatigue life distribution under the progressive stress accelerated life test with inverse power law model. In addition, the practical example and simulation examples are illustrated to show the feasibility of the proposed method.     

威布尔分布组与删失数据下最大似然估计的存在性

本文研究寿命服从威布尔分布,观测数据分组与可能删失的情况下,最大似然估计的存在性,针对所有数据类型,我们给出了最大似然估计存在性的一个充分必要条件,文章结尾讨论了仅一个失效数据时最大似然估计的计算。     

Estimations from the exponentiated rayleigh distribution based on generalized Type-II hybrid censored data

In this article, unknown parameters of exponentiated Rayleigh distribution based on generalized Type II Hybrid censored data, survival function, failure rate function and coefficient of variation are derived by applying the maximum likelihood, Bayes and percentile bootstrap methods. Approximate confidence intervals for the unknown parameters, survival function, failure rate function and coefficient of variation are obtained. We study Bayes estimates under gamma priors distributions depending on symmetric and asymmetric loss functions via the Gibbs within Metropolis-Hasting samplers procedure. Finally, the proposed methods can be understood through illustrating the results of the real data analysis.     

The new Neyman type A beta Weibull model with long-term survivors

For the first time, we propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest follows the Neyman type A distribution and the time to this event has the beta Weibull distribution. This new model can be used to analyze survival data when the hazard rate function is increasing, decreasing, bathtub or unimodal-shaped. It includes some commonly used lifetime distributions and some well-known cure rate models as special cases. Maximum likelihood and non-parametric bootstrap are used to estimate the regression parameters. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform global influence analysis. The usefulness of the new model is illustrated by means of an application in the medical area.