Empirical likelihood method for quantiles with response data missing at random

Empirical likelihood is a nonparametric method for constructing confidence intervals and tests,notably in enabling the shape of a confidence region determined by the sample data.This paper presents a new version of the empirical likelihood method for quantiles under kernel regression imputation to adapt missing response data.It eliminates the need to solve nonlinear equations,and it is easy to apply.We also consider exponential empirical likelihood as an alternative method.Numerical results are presented to compare our method with others.     

Empirical Likelihood Ratio Confidence Interval for Positively Associated Series

Empirical likelihood is discussed by using the blockwise technique for strongly stationary,positivelyassociated random variables.Our results show that the statistics is asymptotically chi-square distributed andthe corresponding confidence interval can be constructed.     

金融资产的CVaR风险的区间估计及假设检验

基于CVaR风险计量技术,论述了正态分布下风险资产的CVaR假设检验方法及置信区间求法,最后用中信指数对我国股市风险情况作了区间估计及显著性检验的实证分析。     

双参数指数分布的可靠寿命的广义置信下限

基于双参数指数分布定数截尾数据,利用Weerahanandi给出的广义置信区间的概念,建立了可靠寿命的广义置信下限,并从理论上证明了我们给出的广义置信下限是精确的,即基于广义置信下限的区间估计的覆盖率等于要求的置信水平.广义置信下限需要通过数值方法得到,但是计算方法是简单直接的.在小样本情形下,通过对基于广义置信下限的置信区间与Engelhardt-Bain近似置信区间覆盖率的模拟比较,发现广义置信下限更令人满意.     

Lincoln-Petersen模型中群体大小的置信限和置信区间

对于Lincoln-Petersen模型,基于捕获-再捕获方法得到的数据,我们给出了群体大小的置信限和置信区间的有效计算方法.     

Tukey两步同时置信区间

利用两阶段抽样,构造出Tukey两步同时置信区间,它同时满足预先给定的可靠度和精度的要求.且利用数值计算的方法给出了第一阶段最优抽样量.     

部分线性度量误差模型中的经验似然推断

部分线性度量误差模型(Partial linear measurement error model)是经典的部分线性模型的推广．在此模型中,我们假定解释变量含有度量误差．本文,我们把经验似然推广到部分线性度量误差模型,得到了非参数的Wilk's定理．我们的方法可以用来构建置信区间(域),也可以用来检验．数值模拟表明,我们的方法在构建的置信区间长度以及覆盖率方面有很好的结果．     

随机套分类模型中方差分量区间估计的改进

首先给出了随机套分类模型中方差分量基于由方差分析产生的平方和的区间估计.然后以此为基础进行了改进,推导出了同时依赖于均值与平方和的区间估计.二者的区间长度相同,但后者有较高的置信度.     

Stein两阶段抽样问题中第一阶段最优抽样量研究

当精度和可靠度给定时，Stein（1945）提出了两阶段抽样方法，构造了同时满足一定可靠度与精度的区间估计．本文则利用数值计算方法，进一步给出了此两阶段抽样中最优的第一阶段抽样量．     

基于置信界及信息附加寻找最大耐受剂量的逐步方法

The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.