可靠性设计中的疲劳裂纹扩展随机模型

首先导出了疲劳裂纹扩展的确定性方程，然后将疲劳裂纹扩展确定性方程随机化，导出了疲劳裂纹随机扩展的概率公式，并推导了疲劳裂纹随机扩展的置信限公式，最后，给出了二种材料疲劳裂纹随机扩展数据的处理结果。     

Empilrical likelihood for non-parametric regression models with missing responses: Multiple design case

Empirical likelihood(EL) ratio statistic on θ = g(x) is constructed based on the inverse probability weighted imputation approach in a nonparametric regression model Y = g(x) + ε(x ∈ [0,1]p) with fixed designs and missing responses,which asymptotically has χ12 distribution.This result is used to obtain a EL based confidence interval on θ.     

广义活动网络ETI支付进度计划问题的研究

优化ETI支付进度安排是消除承包商和业主由于进度安排原因而产生经济纠纷,实现双赢的最有效方法.本文分析了活动间广义时序关系的类型,建立了广义时序关系下活动间的约束关系;在考虑承包商和业主双方联合收益要求的基础上,提出了双方的净现值按同比例减小的折衷原则,构建了广义活动网络在ETI支付方式下的支付进度计划模型;最后通过一个算例进行了分析,验证表明该优化模型具有较好的有效性和实用性.     

Multi-armed bandit with sub-exponential rewards

We consider a general class of multi-armed bandits (MAB) problems with sub-exponential rewards. This is primarily motivated by service systems with exponential inter-arrival and service distributions. It is well-known that the celebrated Upper Confidence Bound (UCB) algorithm can achieve tight regret bound for MAB under sub-Gaussian rewards. There has been subsequent work by Bubeck et al. (2013) [4] extending this tightness result to any reward distributions with finite variance by leveraging robust mean estimators. In this paper, we present three alternative UCB based algorithms, termed UCB-Rad, UCB-Warm, and UCB-Hybrid, specifically for MAB with sub-exponential rewards. While not being the first to achieve tight regret bounds, these algorithms are conceptually simpler and provide a more explicit analysis for this problem. Moreover, we present a rental bike revenue management application and conduct numerical experiments. We find that UCB-Warm and UCB-Hybrid outperform UCB-Rad in our computational experiments.     

基于一种新模型对软件可靠度的估计

本文通过修改JM模型给出了一种新的软件可靠性模型,并对软件可靠度给出了点估计和置信限.对实际数据的分析表明这种新模型的预测能力比JM模型要好.     

贮备系统可靠性之近似置信限

设组成贮备系统的指数寿命型元件的失效率之间满足某种关系．本文给出了这样的两个贮备系统组成的串联系统可靠性之近似置信下限的计算公式．     

Empirical process approach to some two-sample problems based on ranked set samples

We study the asymptotic properties of both the horizontal and vertical shift functions based on independent ranked set samples drawn from continuous distributions. Several tests derived from these shift processes are developed. We show that by using balanced ranked set samples with bigger set sizes, one can decrease the width of the confidence band and hence increase the power of these tests. These theoretical findings are validated through small-scale simulation studies. An application of the proposed techniques to a cancer mortality data set is also provided. Part of the research was conducted while Kaushik Ghosh was visiting Statistical Research and Applications Branch of the National Cancer Institute on an Intergovernmental Personnel Assignment.     

An efficient shrinkage bootstrap bias estimator for smooth functions of sample means

Summary  Since it is not always possible to calculate bootstrap estimators, they are usually approximated by simulation. In this article, we propose a bootstrap bias estimator for smooth functions of sample means that has less mean squared error, due to the simulation process, than the ordinary bootstrap. The estimator is based on shrinking the bootstrap mean towards the original sample mean. It can easily be implemented while demanding almost no additional computational effort.     

指数分布族中矩估计序贯置信区间

在矩估计的基础上,对于给定精度(2d)及置信系数(α),建立了对参数函数g(θ) 的一个序贯置信区间估计的程序．并讨论了在一定条件下,当d→0,它的渐近相合性、渐近有效性及有界的最优费用差(EN(d)-n(d))等渐近性质．     

置信区间的随机加权估计

用随机加权法建立了分位点ｑ的置信区间，又用后向临界点导出了更实用的置信区间。导出的置信区间是非参数的且和原置信区间长度相同。