单向分类随机效应模型中方差分量的渐近最优经验Bayes估计

本文在加权平方损失下导出了单向分类随机效应模型中方差分量的Bayes估计, 利用多元密度及其偏导数的核估计方法构造了方差分量的经验Bayes(EB)估计,证明了 EB估计的渐近最优性.文末还给出了一个例子说明了符合定理条件的先验分布是存在 的.     

随机效应模型中方差分量的Bayes估计及其优良性

在平衡单向分类随机效应模型中,假定方差分量具有共轭先验分布,在加权平方损失下导出了方差分量的Bayes估计;在均方误差准则下研究了方差分量的Bayes估计相对于经典统计方法中的ANOVA估计的优良性.最后,给出了本文主要结果的一个注释.     

加权先验下的Bayes方差估计

针对具有多阶段试验信息的参数估计问题,考虑到历史数据和现场数据的不可等同性,给基于历史数据的似然函数加一个权参数a0(0≤a0≤1)的方案,结合动态修正Bayes估计方法,建构并推导出正态分布方差的加权先验Bayes估计模型.     

随机效应模型中方差分量渐近最优的经验Bayes估计

本文在加权二次损失下导出了双向分类随机效应模型中方差分量的Bayes估计,并利用多元密度函数及其混合偏导数核估计的方法构造了方差分量的经验Bayes(EB)估计.在适当的条件下证明了EB估计的渐近最优性,给出了模型的特例和推广.最后,举出一个满足定理条件的例子.     

无失效数据情形可靠性参数的估计和调整

本文在无失效取样情形下,提出了产品可靠性参数的一种估计和调整的方法———加权多层Bayes估计法.在无失效数据情形下失效率的多层Bayes估计和引进失效信息后失效率的多层Bayes估计的基础上,对可靠性参数进行了估计和调整———给出了失效率和可靠度的加权多层Bayes估计.最后,结合发动机的实际问题进行了计算,结果表明本文提出的方法可行且便于应用.     

非平衡随机效应模型中方差分量的经验Bayes估计

对非平衡单向分类随机效应模型中方差分量找到了其最小充分统计量,在加权平方损失下导出了其Bayes估计,利用多元密度及其偏导数的核估计方法构造了方差分量的经验Bayes(EB)估计,并导出了其收敛速度.文末用例子说明了符合定理条件的先验分布是存在的.     

右删失数据下生存函数的随机加权估计

本文利用随机加权思想提出了右删失数据下生存函数的随机加权估计,证明了所给的随机加权估计的强相合性.     

基于最优估计的数据融合理论

本文提出了一种最优加权的数据融合方法，分析了最优权值的分配原则；给出了多源信息统一的线性融合模型，使其表示不受数据类型和融合系统结构的限制，并指出在噪声协方差阵正定的前提下，线性最小方差估计融合和加权最小二乘估计融合是等价的；介绍了数据融合中的Bayes极大后验估计融合方法，给出了利用极大后验法进行传感器数据融合的一般表示公式；最后以两传感器数据融合为例，证明了利用Bayes极大后验估计进行两传感器数据融合所得到的融合状态的精度比相同条件下极大似然估计得到的精度要高，同时它们均优于任一单传感器局部估计精度。     

广义Pareto分布参数的经验Bayes估计的收敛速度

在加权平方损失函数下,获得广义Pareto分布形状参数的经验Bayes(EB)估计,并得到了该估计的收敛速度.     

刻度指数族参数的经验Bayes估计的收敛速度

本文对刻度指数族在加权平方损失下获得了参数的Bayes估计,并构造了相应的经验Bayes（EB）估计,证明了所提出的EB估计是渐近最优的且有收敛速度,其中1/2≤λ<1,s≥3是一给定的整数.最后,给出了刻度指数族EB估计的两个应用.     

Empirical Bayes inference for the Weibull model

In this study, the theory of statistical kernel density estimation has been applied for deriving non-parametric kernel prior to the empirical Bayes which frees the Bayesian inference from subjectivity that has worried some statisticians. For comparing the empirical Bayes based on the kernel prior with the fully Bayes based on the informative prior, the mean square error and the mean percentage error for the Weibull model parameters are studied based on these approaches under both symmetric and asymmetric loss functions, via Monte Carlo simulations. The results are quite favorable to the empirical Bayes that provides better estimates and outperforms the fully Bayes for different sample sizes and several values of the true parameters. Finally, a numerical example is given to demonstrate the efficiency of the empirical Bayes.     

Hierarchical Bayes variable selection and microarray experiments

Hierarchical and empirical Bayes approaches to inference are attractive for data arising from microarray gene expression studies because of their ability to borrow strength across genes in making inferences. Here we focus on the simplest case where we have data from replicated two colour arrays which compare two samples and where we wish to decide which genes are differentially expressed and obtain estimates of operating characteristics such as false discovery rates. The purpose of this paper is to examine the frequentist performance of Bayesian variable selection approaches to this problem for different prior specifications and to examine the effect on inference of commonly used empirical Bayes approximations to hierarchical Bayes procedures. The paper makes three main contributions. First, we describe how the log odds of differential expression can usually be computed analytically in the case where a double tailed exponential prior is used for gene effects rather than a normal prior, which gives an alternative to the commonly used B-statistic for ranking genes in simple comparative experiments. The second contribution of the paper is to compare empirical Bayes procedures for detecting differential expression with hierarchical Bayes methods which account for uncertainty in prior hyperparameters to examine how much is lost in using the commonly employed empirical Bayes approximations. Third, we describe an efficient MCMC scheme for carrying out the computations required for the hierarchical Bayes procedures. Comparisons are made via simulation studies where the simulated data are obtained by fitting models to some real microarray data sets. The results have implications for analysis of microarray data using parametric hierarchical and empirical Bayes methods for more complex experimental designs: generally we find that the empirical Bayes methods work well, which supports their use in the analysis of more complex experiments when a full hierarchical Bayes analysis would impose heavy computational demands.     

相依非线性回归系统中的附加信息Bayes拟似然

对多个相依统计模型的研究,现有成果主要集中在相依线性回归系统.本文则首次提出多个相依非线性回归系统中的附加信息Bayes拟似然,给出误差相关信息和先验信息在拟似然中的迭加方法,在较弱的条件下得到附加信息Bayes拟似然的一些性质,在Bayes风险准则下。讨论了其估计函数和参数估计的最优性,证明了附加信息Bayes拟似然的渐近 Bayes风险随着相依信息的增力。而逐步减少.     

The empirical bayes rules with floating optimal sample size for exponential conditional distributions

Summary We consider the empirical Bayes solution in such a situation where the sample size is successively determined by a rule which includes the Bayes risks and the observation costs. The empirical Bayes floating optimal sample size depends on current as well as on previous information assumed to be collected from earlier performances of similar decisions. The sampling is done from an exponential conditional distribution, with a single parameter. The proofs, which show the asymptotic optimality of the empirical Bayes solution, are presented for a hypotheses-testing problem. A straight generalization to a multiple decision problem is also given.     

逐步增加首失效截尾样本下参数估计的优良性

在对称平方损失函数下, 利用逐步增加首失效截尾样本, 研究两参数Pareto分布族参数的一致最小方差无偏估计(UMVUE), Bayes估计和参数型经验Bayes(PEB)估计. 按照均方误差(MSE)准则, 比较UMVUE与PEB估计的优良性. 根据风险函数导出Bayes估计与PEB估计的渐近性, 并获得它们的收敛速度. 在相同的置信水平下, 研究参数分别在经典统计和Bayes统计中的区间估计, 并利用数值模拟说明Bayes区间估计的精度高于经典统计区间估计.     

基于R软件的Bayes判别分析在医学中的应用

Bayes判别在进行判别分析时考虑到各总体出现的先验概率、预报的先验概率及错判造成的损失,其判别效能优于其他判别方法.对Bayes判别方法详细介绍的基础上,利用R软件对一组舒张压和胆固醇数据分别进行Bayes判别分析、Fisher判别分析和基于距离的判别分析,对比三种不同方法下得到的判别结果,结果表明Bayes判别分析得到的分类结果精度较高,Bayes判别分析在医学领域有较好的应用前景.     

指数分布基于多重定数截尾样本的近似Bayes估计

该文讨论了指数分布场合具有多重定数截尾样本的一种新的近似Bayes估计,导出了基于寿命试验数据的近似Bayes估计和在步加寿命试验情形基于多重定数截尾样本的近似无偏估计和近似Bayes估计.利用模拟方法研究了所给估计的精度,模拟结果显示所给估计的精度是令人满意的     

非对称损失函数下Burr Ⅻ型分布可靠性指标的Bayes估计

本文研究了R=P(Y＜X)在两种非对称损失函数下的Bayes估计问题,其中随机变量X和Y相互独立且服从不同的Burr XII型分布.利用Lindley近似方法,获得了Bayes估计的显式近似表达式,通过随机模拟比较了不同损失函数下的Bayes估计的性质.     

定数截尾两参数指数——威布尔分布形状参数的Bayes估计

在不同的损失函数下,本文研究了两参数指数—威布尔分布(EWD)形状参数的Bayes估计问题.基于定数截尾试验,当其中一个形状参数α已知时,给出了另一个形状参数θ在三种不同损失函数下的Bayes估计表达式,并求得了可靠度函数的Bayes点估计.最后运用随机模拟方法,将Bayes估计和极大似然估计进行了比较.结果表明,LINEX损失下Bayes估计的精度比极大似然估计高.     

Bayes empirical Bayes estimation of a poisson mean

In the empirical Bayes (EB) decision problem consisting of squared error estimation of a Poisson mean, a prior distribution λ is placed on the gamma family of prior distributions to produce Bayes EB estimators which are admissible. A subclass of such estimators is shown to be asymptotically optimal (a.o.). The results of a Monte Carlo study are presented to demonstrate the favorable a.o. property of the Bayes EB estimators in comparison with other competitors.