雷管准爆率的几种估计方法

雷管的准爆率如何估计？本文给出矩估计法、贝叶斯估计法、区间估计法，并对各种方法加以分析比较，认为贝叶斯估计法在工程爆破中最为适用。     

基于AMLE的高光谱本征维数估计方法研究

最大似然估计法（MLE）能快速计算高光谱影像数据的本征维数,原理简单,但它不是一种很好的本征维数估计法。因为它存在忽略单个数据点贡献的缺点,导致冗余信息放大、重要信息被湮没,不利于特征提取和分类。对此．本文引入自适应性最大似然估计法（AMLE）。该方法通过引入权函数,突出每个数据点的贡献,能提取出被最大似然估计法湮没的重要特征。在对高光谱影像进行特征提取及分类的试验中验证了该方法的有效性。     

大跨度高空钢结构连廊的有限元模型修正

使用基于灵敏度分析和贝叶斯估计的有限元模型修正方法对复杂连体高层建筑群杭州市民中心的高空钢结构连廊进行了有限元模型修正.通过频率误差和模态置信因子计算,比较分析了修正前连廊数值模型的模态参数和基于环境激励下实测得到的连廊模态参数,以确定数值模型动力特性与实际结构动力特性的差异.通过模态参数对连廊构件物理参数的敏感度分析构造灵敏度矩阵,并在此基础上筛选出对结构模态参数影响程度大的参数.基于贝叶斯估计法构造参数加权矩阵和响应加权矩阵,根据待修正参数初始设计值的置信度设置参数允许的上下边界值,按一定的收敛准则进行迭代修正.修正后连廊模型的动力特性与实测动力特性有很好的一致性,修正模型可以用来对连廊进行振动控制、响应预测及性态评估.     

关于贝叶斯估计的进一步讨论

文章则从绝对差损失函数与相对差损失函数出发 ,先导出两种新的贝叶斯解——中值估计和积分比估计 ;然后再对实际问题进行具体应用 ;在选定同类先验分布条件下将以上结果与传统的条件期望估计、最大后验估计相比较 ,探讨了各类贝叶斯解的优良性 .     

抗差估计在GPS网空间坐标系转换中的应用

本文将抗差估计理论运用于GPS网空间坐标系间转换参数的求解,从理论上阐述了抗差估计求参的数学模型及精度评价指标。同时结合实例,将抗差估计与最小二乘估计在GPS网空间坐标系间转换参数求解应用进行分析比较,结果表明当转换基准点存在不稳定点时,采用抗差估计法能有效剔除粗差,其计算结果精度明显高于最小二乘估计法。     

可靠性工程中参数的一种估计方法

提出了可靠性工程中参数的一种估计方法——新Bayes估计法，给出了失效概率、失效率的新Bayes估计的定义及其新Bayes估计。最后，结合实际问题的数据，进行了具体计算和分析，结果表明所提出的新Bayes估计法有效、可行，便于工程技术人员在工程中应用。     

按摩模式对揉捏式按摩椅按摩舒适性的影响

以揉捏式按摩椅作为研究对象,采用自由模量幅度估计法.通过对标准化后的按摩舒适度评价值分别进行统计,得到颈肩、背部和腰部区域3个区域共计8个测点最舒适的按摩模式,并通过脑电测试实验从客观角度验证了对由自由模量幅度估计法所得的各按摩区域最佳按摩模式的科学性,探讨了按摩模式对按摩椅按摩舒适性的影响.     

既有结构材料强度的信度推断方法

既有结构当前的材料强度是客观上完全确定但具有主观不确定性的未确知量.目前的区间估计法为概率推断方法,并不完全适用于对既有结构当前现实的材料强度的推断.该文针对既有结构的特点,分析了目前区间估计法的缺陷,提出了既有结构当前材料强度的信度推断方法.该法具有良好的理论基础,不仅考虑了测试构件与未测试构件之间的差别,而且能够合...     

平衡损失函数下Cox模型的可靠性分析:记录值样本情形

本文在记录值样本下,分别讨论了一类指数分布可靠性指标的经验贝叶斯估计以及未来失效样本的预测问题.通过ML-Ⅱ方法估计超参数,进而在平衡均方损失和平衡Linex损失下,获得了相关指标的经验贝叶斯估计,并给出未来记录值样本的经验贝叶斯预测区间.利用Monte-Carlo模拟方法给出了一个数值算例,研究了结果的精确性.     

基于物理参数贝叶斯更新的桥梁剩余强度估计研究

考虑系统参数的随机性,将基于广义卡尔曼滤波的子结构法与贝叶斯更新方法相结合,提出了桥梁结构基于贝叶斯更新物理参数的剩余强度估计两步法:第一步,将子结构法与广义卡尔曼滤波算法相结合,成功识别出子结构及其相邻单元的物理参数;第二步,视识别出的结构物理参数为更新信息,对以蒙特卡罗仿真实验结果作为先验分布的参数进行贝叶斯更新并分别基于蒙特卡罗仿真参数和贝叶斯更新物理参数对结构进行了剩余强度估计。数值算例表明:基于贝叶斯更新物理参数估计得到的结构剩余强度明显低于基于蒙特卡罗仿真参数估计得到的结构剩余强度。该方法为测量响应信息不完备条件以及小样本抽样情况下桥梁结构剩余强度估计提供了一个较好的解决思路。     

单桩承载力可靠度分析中试桩信息的应用

Bayes方法是统计学决策方法的基础之一,通过采样,修改先验的概率分布,从而减少了参数的不确定性。本文针对港工中实际试桩少,难以对单桩承载力作出准确统计的问题,用Bayes方法进行了计算,并考虑了主观不定性对抗力的影响,较好地解决了实际问题。     

Statistical inference for constant-stress accelerated life tests with dependent competing risks from Marshall-Olkin bivariate exponential distribution

This paper considers a constant-stress accelerated dependent competing risks model under Type-II censoring. The dependent structure between competing risks is modeled by a Marshall-Olkin bivariate exponential distribution, and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability function under the normal usage condition at mission time are obtained by using the maximum likelihood estimation method and the bootstrap sampling technique. Moreover, the pivotal quantities based estimation are adopted to estimate the model parameters and the generalized confidence intervals. As a comparison, we also consider the Bayes estimation and the highest posterior density credible intervals for the model parameters based on conjugate priors and importance sampling method, respectively. To illustrate the proposed methodology, a Monte Carlo simulation is used to study the performances of different estimation methods. Finally, a dataset is analyzed for illustrative purpose and a comparison with the original results is also given.     

An alternative perspective on the mixture estimation problem

The paper presents an alternative perspective on the mixture estimation problem. First, observations are counted into a histogram. Secondly, rough and enhanced parameter estimation followed by the separation of observations is done. Finally, the residue is distributed between the components by the Bayes decision rule. The number of components, the mixture component parameters and the component weights are modelled jointly, no initial parameter estimates are required, the approach is numerically stable, the number of components has no influence upon the convergence and the speed of convergence is very high. The alternative perspective is compared to the EM algorithm and verified through several data sets. The presented algorithm showed significant advantages compared to the competitive methods and has already been successfully applied in reliability and fatigue analyses.     

双参数Rayleigh分布环境因子的经验Bayes估计及应用

本文针对Rayleigh分布位置参数已知的情形,给出了Rayleigh分布环境因子的极大似然估计和经验Bayes估计,并将环境因子的估计结果应用于Rayleigh部件的可靠性评估,给出了该部件可靠度函数与失效率的估计。最后的随机模拟例子表明,经验Bayes估计优于极大似然估计,并且在考虑环境因子的情形下,Rayleigh部件可靠性指标的估计优于未考虑环境因子时的估计。     

Bayes estimation of residual life by fusing multisource information

Residual life estimation is essential for reliability engineering. Traditional methods may experience difficulties in estimating the residual life of products with high reliability, long life, and small sample. The Bayes model provides a feasible solution and can be a useful tool for fusing multisource information. In this study, a Bayes model is proposed to estimate the residual life of products by fusing expert knowledge, degradation data, and lifetime data. The linear Wiener process is used to model degradation data, whereas lifetime data are described via the inverse Gaussian distribution. Therefore, the joint maximum likelihood (ML) function can be obtained by combining lifetime and degradation data. Expert knowledge is used according to the maximum entropy method to determine the prior distributions of parameters, thereby making this work different from existing studies that use non-informative prior. The discussion and analysis of different types of expert knowledge also distinguish our research from others. Expert knowledge can be classified into three categories according to practical engineering. Methods for determining prior distribution by using the aforementioned three types of data are presented. The Markov chain Monte Carlo is applied to obtain samples of the parameters and to estimate the residual life of products due to the complexity of the joint ML function and the posterior distribution of parameters. Finally, a numerical example is presented. The effectiveness and practicability of the proposed method are validated by comparing it with residual life estimation that uses non-informative prior. Then, its accuracy and correctness are proven via simulation experiments.     

删失下的指数分布的贝叶斯估计

本文分别在Ⅱ型删失和随机删失下,表明了共轭先验下的指数分布的刻度参数的贝叶斯估计为具有如下形式的收缩估计(?)_(BE)=a■ bEθ,此处■为依赖样本θ的一个无偏估计且Eθ表示先验分布的期望。当采用平方损失函数时,a b=1;如果用加权平方损失函数,则a b＜1。     

Reliability analysis of the dynamic system for the Chen model through sequential order statistics

Recently, the two-parameter Chen distribution has widely been used for reliability studies in various engineering fields. In this article, we have developed various statistical inferences on the composite dynamic system, assuming Chen distribution as a baseline model. In this dynamic system, failure of a component induces a higher load on the surviving components and thus increases component hazard rate through a power-trend process. The classical and Bayesian point estimates of the unknown parameters of the composite system are obtained by the method of maximum likelihood and Markov chain Monte Carlo techniques, respectively. In the Bayesian framework, we have used gamma priors to obtain Bayes estimates of unknown parameters under the squared error and generalized entropy loss functions. The interval estimates of the baseline reliability function are obtained by using the Fisher information matrix and Bayesian method. A parametric hypothesis test is presented to test whether the failed components change the hazard rate function. A compact simulation study is carried out to examine the behavior of the proposed estimation methods. Finally, one real data analysis is performed for illustrative purposes.     

A suboptimal estimation algorithm based on smoothing B splines

Smoothing cubic normalized B splines are used in synthesizing a suboptimal algorithm for estimation on Bayes criteria, maximum likelihood, and a posteriori density without constraint on the gaussian behavior of the corresponding distribution densities. The potential accuracy of the algorithm is evaluated in accordance with the Cramer-Rao inequality. __________ Translated from Izmeritel’naya Tekhnika, No. 10, pp. 14–17, October, 2006.     

Estimating rate of occurrence of rare events with empirical bayes: A railway application

Classical approaches to estimating the rate of occurrence of events perform poorly when data are few. Maximum likelihood estimators result in overly optimistic point estimates of zero for situations where there have been no events. Alternative empirical-based approaches have been proposed based on median estimators or non-informative prior distributions. While these alternatives offer an improvement over point estimates of zero, they can be overly conservative. Empirical Bayes procedures offer an unbiased approach through pooling data across different hazards to support stronger statistical inference.This paper considers the application of Empirical Bayes to high consequence low-frequency events, where estimates are required for risk mitigation decision support such as as low as reasonably possible. A summary of empirical Bayes methods is given and the choices of estimation procedures to obtain interval estimates are discussed. The approaches illustrated within the case study are based on the estimation of the rate of occurrence of train derailments within the UK. The usefulness of empirical Bayes within this context is discussed.