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．     

Rényi’s Problem in Short Intervals

In this paper, we study Rényi’s problem and other related problems about the additive functions in short intervals. As corollaries, we improve Ivic’s results. This work is supported by National Natural Science Foundation of China (10301018)     

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.     

非等间距组合灰色预测模型

对于非等间距原始数据序列,根据灰色预测模型建模特点,提出了一类非等间距灰色组合预测方法,弥补了传统非等间距原始数据预测模型的不足,提高了灰色预测的精度.实例表明结果理想可靠,有较好的实际意义.     

Banach空间中一阶微分方程的无穷边值

利用M nch不动点定理研究了Banach空间中一类无穷区间上的一阶微分方程解的存在性     

群决策中判断的一致性分析及方案排序

对群决策中基于区间数互补判断矩阵形式偏好信息的一致性问题进行了探讨。首先,给出了区间数互补判断矩阵相容性指标定义及性质;然后,通过定义群和个体判断的一致性指标,提出了群判断的一致性判别方法和不一致的协调方法;同时依据个体判断的一致性指标大小,即群成员评判水平的高低,给出了群偏好信息的集结方法;最后给出了由区间数互补判断矩阵导出方案排序权重的计算方法及应用算例。     

Risk measures, distortion parameters, and their empirical estimation

Risk measures are of considerable current interest. Among other uses, they allow an insurer to calculate a risk-loaded premium for a random loss. However, the premium principle in use by the insurer may be, at least in part, based on considerations other than risk. It is then important to quantify the degree to which the premium compensates the insurer for the risk associated with the loss. This can be done by choosing a suitable risk measure and solving for the parameter that leads to the insurer’s premium. When the loss distribution is unknown, this becomes a statistical estimation problem.In this paper, we investigate the nonparametric estimation of the parameter associated with a distortion-based risk measure. It is assumed that the premium principle is known, but no information is assumed about the loss distribution, and therefore empirical estimators are used. We explore the asymptotic properties of the resulting estimator of the risk measure parameter in general and for three well-known risk measures in particular: the proportional hazards transform, the Wang transform, and the conditional tail expectation.     

Rank‐tolerance graph classes

In this article we introduce certain classes of graphs that generalize ?‐tolerance chain graphs. In a rank‐tolerance representation of a graph, each vertex is assigned two parameters: a rank, which represents the size of that vertex, and a tolerance which represents an allowed extent of conflict with other vertices. Two vertices are adjacent if and only if their joint rank exceeds (or equals) their joint tolerance. This article is concerned with investigating the graph classes that arise from a variety of functions, such as min, max, sum, and prod (product), that may be used as the coupling functions ? and ρ to define the joint tolerance and the joint rank. Our goal is to obtain basic properties of the graph classes from basic properties of the coupling functions. We prove a skew symmetry result that when either ? or ρ is continuous and weakly increasing, the (?,ρ)‐representable graphs equal the complements of the (ρ,?)‐representable graphs. In the case where either ? or ρ is Archimedean or dual Archimedean, the class contains all threshold graphs. We also show that, for min, max, sum, prod (product) and, in fact, for any piecewise polynomial ?, there are infinitely many split graphs which fail to be representable. In the reflexive case (where ? = ρ), we show that if ? is nondecreasing, weakly increasing and associative, the class obtained is precisely the threshold graphs. This extends a result of Jacobson, McMorris, and Mulder [10] for the function min to a much wider class, including max, sum, and prod. We also give results for homogeneous functions, powers of sums, and linear combinations of min and max. © 2006 Wiley Periodicals, Inc. J Graph Theory     

树的补图的区间图完全化问题

一个图G的区间图完全化问题包含两类子问题:侧廓问题和路宽问题,分别表示为P(G)和PW(G),其中侧廓问题是寻求G的一个边数最小的区间超图;路宽问题是寻求G的一个团数最小的区间超图.这两类子问题分别在数值代数、VLSI-设计和算法图论等学科领域中有重要的应用.对一般图来说,两类子问题都是NP-完全问题;但是对一些特殊图类来说,它们在多项式时间内可解.本文给出了树T的补图的具体侧廓和路宽值.     

Likelihood-based Inference for the Ratios of Regression Coefficients in Linear Models

We consider the standard linear multiple regression model in which the parameter of interest is the ratio of two regression coefficients. Our setup includes a broad range of applications. We show that the 1− α confidence interval for the interest parameter based on the profile, conditional profile, modified profile or adjusted profile likelihood can potentially become the entire real line, while appropriately chosen integrated likelihoods do not suffer from this drawback. We further explore the asymptotic length of confidence intervals in order to compare integrated likelihood-based proposals. The analysis is facilitated by an orthogonal parameterization.