一类Minimax分式规划问题的迭代算法

对一类Minimax分式规划问题(MFP)提出一个迭代算法.首先通过引进变量和指数变换,将问题(MFP)等价转化为问题(Q),然后利用代数-几何平均不等式以及合适的转化过程,将等价问题(Q)压缩为凸规划问题(Q).从而根据选择不同的点所对应的压缩问题(Q),将原问题的求解过程转化为求解一系列的凸规划问题.数值实验表明算法是可行有效的.     

线性回归模型的一类新约束型LIU估计

针对带线性约束型的回归模型复共线性问题,提出了一种新估计,称之为修正约束型LIU估计,给出了新估计的性质.在均方误差准则基础上证明了在一定条件下,修正约束型LIU估计优于最小二乘估计、岭估计、修正岭估计和约束型LIU估计,最后讨论了新估计的可容许性.     

正态分布均值变点的小波检验和估计

本文探究的是方差不变的条件下服从正态分布的随机序列均值变点问题,利用小波方法检测和估计均值变点.考虑存在一个或多个均值变点,利用小波方法构造检测均值变点的统计量,并且估计均值变点的个数、位置以及跳跃度,最后通过模拟仿真验证有限样本下本文方法的有效性.     

基于跳-扩散过程的振动系统的模态识别方法(英文)

本文提出了Lévy激励下LTI系统的一种时域模态识别方法.系统响应可看做是一个跳-扩散过程.基于二次变差和多幂次变差的性质,跳-扩散过程被分解成扩散过程和纯跳激励的过程,二者都具有和原系统相同的未知参数.最后通过扩散过程的极大似然估计方法来估计Lévy激励下LTI系统的参数.数值结果表明该方法估计精度高.     

The effect of target function on the predictive performance of the two‐stage ridge estimator

The main thrust of this study is to consider the problem of simultaneous prediction of actual and average values of the simultaneous equations model through the target function of Shalabh (Bulletin of International Statistical Institute, 1995, 56, 1375–1390). We focus on the predictive performance of the two‐stage ridge estimator with the motivation for eliminating the disorder arising from multicollinearity. An optimal biasing parameter of the two‐stage ridge estimator is derived by a minimization process of prediction mean square error. In addition, an optimal estimator for the weight of observed value in target function is attained theoretically. The results inferred from a numerical example and a Monte Carlo experiment provide a dramatic improvement in the predictive ability of the two‐stage ridge estimator.     

NSD样本最近邻密度估计的相合性

利用负超可加相依(NSD)序列的Bernstein不等式和Borel-Cantelli引理, 给出NSD样本最近邻密度估计和失效率函数估计的(弱)强相合性、 一致强相合性和(一致)强相合速度.     

几乎无偏刘估计的不可容许性

考虑了线性回归模型中,在Fisherian和Mahalanobis损失函数下,几乎无偏刘估计对于最小二乘估计的不可容许性;结论表明:几乎无偏刘估计在Mahalanobis损失函数下是不可容的;最后进行了数值模拟来表明结果.     

Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

The widespread use of Location-Based Services(LBSs),which allows untrusted service providers to collect large quantities of information regarding users’locations,has raised serious privacy concerns.In response to these issues,a variety of LBS Privacy Protection Mechanisms(LPPMs)have been recently proposed.However,evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics.In particular,the relationships between these metrics have not been examined in depth under a common adversarial model,leading to a possible selection of the inappropriate metric,which runs the risk of wrongly evaluating LPPMs.In this paper,we address these issues by proposing a privacy quantification model,which is based on Bayes conditional privacy,to specify a general adversarial model.This model employs a general definition of conditional privacy regarding the adversary’s estimation error to compare the different LBS privacy metrics.Moreover,we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics.We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective.Our results contribute to a better understanding of how privacy properties can be measured,as well as to the better selection of the most appropriate metric for any given LBS application.     

WOD样本密度函数和失效率函数递归核估计的逐点强相合性

考虑同分布宽象限相依(WOD)随机样本未知密度函数的一类递归型密度核估计量.利用WOD序列的Rosenthal型不等式,在一定条件下证明了该估计量的逐点强相合性,并讨论了失效率函数估计的逐点强相合性.     

广义帕累托分布的应力-强度可靠性的贝叶斯估计

基于不同形状参数的广义帕累托分布,讨论应力-强度参数的贝叶斯估计.通过模拟得出在平方损失函数和0-1损失函数下的贝叶斯估计值比较相近;有先验信息条件下的贝叶斯估计的均方误差值低于无信息先验条件下的贝叶斯估计的均方误差值.