区间数据均值的经验似然估计

本文提出了估计区间数据均值的经验似然方法,通过构造区间数据的无偏转换,导出了渐近服从χ2分布的对数经验似然函数,从而得到了均值的置信区间.通过若干模拟例子说明,用本文提出的方法得到的估计,优于用渐近正态法得到的估计.     

Gamma分布环境因子的统计分析

证明了Gamma分布环境因子的最大似然估计是有偏估计,且其偏差为正,进而导出了Gamma分布环境因子的近似无偏估计.利用Cornish-Fisher展开导出了Gamma分布环境因子的广义置信区间,另外也给了Gamma分布环境因子的Bootstrap-t置信区间.利用模拟方法研究了所给近似无偏估计和区间估计的精度,模拟结果显示所给近似无偏估计和区间估计的精度是相当好的.     

Gamma恒定应力加速寿命试验的统计分析

证明了基于恒定应力加速寿命试验数据Gamma模型参数的最大似然估计在一定条件下存在,进而导出了Gamma模型参数的备择估计.利用Cornish-Fisher展开导出了Gamma形状参数的近似置信区间,另外也给了Gamma模型的其它参数和正常应力水平下产品寿命的一些重要可靠性指标的广义置信区间.利用模拟方法研究了所给点估计和区间估计的精度,模拟结果显示所给点估计和区间估计的精度是相当好的.     

再入飞行器的最大飞行距离的似然比鉴定（1）

当无验前信息可用时，本文导出了再入飞行器最大飞行距离的似然比检验统计量，由此构造出的置信区间优于由非中心ｔ分布构造的对称截尾置信区间。最后，对两个常用的最大飞行距离的概率水平，０．９５和０．９９。以及对不同的子样数和置信水平，列出了构造这种似然比置信区间所需要的常系数，ｃ１和ｃ２的取值表。     

再入飞行器的最大飞行距离的似然比鉴定(Ⅰ)

当无验前信息可用时,本文导出了再入飞行器最大飞行距离的似然比检验统计量,由此构造出的置信区间优于由非中心t分布构造的对称截尾置信区间.最后,对两个常用的最大飞行距离的概率水平.0.95和0.99.以及对不同的子样数和置信水平,列出了构造这种似然比置信区间所需要的常系数,c_1和c_2的取值表.     

指数分布场合系统应力强度模型可靠度的统计推断

利用最大似然估计和广义区间估计方法,研究了元件的强度和承受的应力均服从指数分布时系统应力强度模型可靠度的估计问题,导出了可靠度的最大似然估计和广义区间估计.同时也讨论了模型的拟合检验问题.利用模拟方法研究了提出的广义置信区间的覆盖率和拟合检验的功效,模拟结果表明提出的广义置信区间的覆盖率与名义置信系数是一致的,提出的拟合检验的功效是好的.最后用一个例子说明提出的方法.     

线性测量误差模型的平均估计

频率模型平均估计近年来受到了较大的关注,但对有测量误差的观测数据尚未见到任何研究.文章主要考虑了线性测量误差模型的平均估计问题,导出了模型平均估计的渐近分布,基于Hjort和Claeskens(2003)的思想构造了一个覆盖真实参数的概率趋于预定水平的置信区间,并证明了该置信区间与基于全模型正态逼近所构造的置信区间的渐近等价性.模拟结果表明当协变量存在测量误差时,模型平均估计能明显增加点估计的效率.     

Laplace分布位置尺度参数的估计比较

研究了Laplace分布位置与尺度参数的估计,首先利用截面似然法获得参数的MLE;然后根据Pitman积分,在其中一个参数固定的情形下,可求得另一个参数的MREE;再通过构造完备充分统计量得到参数的UMVUE.在此基础上导出参数估计的枢轴量及其置信区间,最后用随机数来模拟几种估计的精度.     

条件密度置信区间的覆盖精度

本文用经验似然方法讨论了条件密度的置信区间的构造. 通过对覆盖概率的Edgeworth展开得到了经验似然置信区间的覆盖精度, 同时证明了条件密度的经验似然置信区间的Bartlett可修正性     

有关假设检验的问题

针对假设检验教学引发的问题．阐述了Neyman-Pearson假设检验和置信区间的对偶性．给出用假设检验确定置信区间的做法．     

回归函数核估计的随机加权法

本文利用随机加权法的思想,构造了回归函数g(x)的核估计     

条件密度双重核估计误差分布的逼近速度

In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y!|x) are studied. The results may be used to construct the confidence interval of f(y|x).     

APPROXIMATION RATES OF ERROR DISTRIBUTION OF DOUBLE KERNEL ESTIMATES OF CONDITIONAL DENSITY

§ 1　IntroductionLet(X,Y) be a random vector taking values Rp×Rqand assume that with given X=x,f(y|x) is the conditional density of Y,the Borel-measurable function on(x,y) ,X has amarginal distribution function F(x) and a marginal density function f(x) .Let(X1 ,Y1 ) ,...,(Xn,Yn) be i.i.d.sample taking values in(X,Y) .A class of double kernel esti-mates of f(y|x) proposed by Zhao Linchang and Liu Zhijun[1 ] has the formfn(y|x) = ni=1K1Xi -xan K2Yi -ybn bqn nj=1K1Xj-xan ,(1 .1 )where…     

Random weighting method for Cox’s proportional hazards model

Variance of parameter estimate in Cox’s proportional hazards model is based on asymptotic variance. When sample size is small, variance can be estimated by bootstrap method. However, if censoring rate in a survival data set is high, bootstrap method may fail to work properly. This is because bootstrap samples may be even more heavily censored due to repeated sampling of the censored observations. This paper proposes a random weighting method for variance estimation and confidence interval estimation for proportional hazards model. This method, unlike the bootstrap method, does not lead to more severe censoring than the original sample does. Its large sample properties are studied and the consistency and asymptotic normality are proved under mild conditions. Simulation studies show that the random weighting method is not as sensitive to heavy censoring as bootstrap method is and can produce good variance estimates or confidence intervals.     

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The log-normal distribution is a common choice for modeling positively skewed data arising from many practical applications.This article introduces a new method of constructing confidence interval for a common mean shared by several log-normal populations through confidence distributions, which combines all information from independent sources. We develop a non-trivial weighting approach by taking account of the sample variances of related quantities to enhance efficiency. Combined confidence distributions are used to construct confidence intervals for the common mean and a simplified version of one existing method is also proposed. We conduct simulation studies to evaluate the performance of the proposed methods in comparison with existing methods. Our simulation results show that the weighting approach yields shorter interval length than the non-weighting approach. The newly proposed confidence intervals perform very well in terms of empirical coverage probability and average interval length. Finally, applications of the proposed methodology is illustrated through three real data examples.     

Inference for the Mean Difference in the Two-Sample Random Censorship Model

Inference for the mean difference in the two-sample random censorship model is an important problem in comparative survival and reliability test studies. This paper develops an adjusted empirical likelihood inference and a martingale-based bootstrap inference for the mean difference. A nonparametric version of Wilks' theorem for the adjusted empirical likelihood is derived, and the corresponding empirical likelihood confidence interval of the mean difference is constructed. Also, it is shown that the martingale-based bootstrap gives a correct first order asymptotic approximation of the corresponding estimator of the mean difference, which ensures that the martingale-based bootstrap confidence interval has asymptotically correct coverage probability. A simulation study is conducted to compare the adjusted empirical likelihood, the martingale-based bootstrap, and Efron's bootstrap in terms of coverage accuracies and average lengths of the confidence intervals. The simulation indicates that the proposed adjusted empirical likelihood and the martingale-based bootstrap confidence procedures are comparable, and both seem to outperform Efron's bootstrap procedure.     

On a confidence interval of given length for the parameter of the binomial and the Poisson distributions

Summary The length of the confidence interval which is constructed by one stage sampling is a random variable, whereas we can construct a confidence interval of given length and of given confidence coefficient by a sequential sampling. Of cource, the sample size required by this method is a random variable. This type of problem has already been discussed in several papers when the population is normal (see references [2], [3], [4]). In this paper we shall discuss this subject in the binomial and Poisson cases.     

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本文利用广义p-值和广义置信区间的概念构造 含有三个随机效应的套误差分量模型中方差分量的几种新的精确检验和置信区间, 并讨论它们在尺度变换下的不变性. 模拟结果表明, 基于广义p-值的检验很好地控制了犯第一类错误的概率.     

在模糊随机因素影响下的多层圆筒结构的稳态热传导问题的区间数解法

在多层圆筒结构稳态热传导分析中,根据给定固体壁两侧表面温度总传热量公式,首先推导出当边界温度为随机变量情况下总传热量函数统计参数的均值和方差;然后推导出在导热系数为模糊数,边界温度为随机数下的总传热量的区间表达式．通过比较可以知道由区间数算法得到的区间最大,由概率统计算法得到的区间最小．并给出了两者的相对误差公式．最后引进粗糙集中的上、下近似集,提出用一个参数来统一定义模糊和随机区间进行稳态结构的热传导分析．     

A stochastic programming model to find optimal sample sizes to estimate unknown parameters in an LP

An LP is considered where the technology coefficients are unknown and random samples are taken to estimate them. A stochastic programming problem is formulated to find the optimal sample sizes where it is required that a confidence interval should cover the unknown deterministic optimum value by a given probability and the cost of sampling be minimum.