残差自回归半参数模型的参数估计

研究了残差自回归半参数模型的参数估计,运用广义最小二乘法估计了参数部分．用随机模拟说明了运用广义最小二乘（GLSE）估计出的参数部分优于运用普通最小二乘法（OKSE）得到的估计．     

非寿险分类费率模型的比较研究和实证分析

非寿险分类费率的厘定通常采用的方法有单项分析法、最小偏差法和广义线性模型,特别是后面两种方法在非寿险实务中应用十分广泛,精算文献中对这两种方法的理论和应用研究也较多,但对二者的比较研究较少。本文首先对最小偏差模型和广义线性模型进行了简要介绍,之后对这两种分类费率模型进行了系统的比较研究,总结了它们各自的优缺点以及二者之间的一些等价关系,最后通过一组实际的汽车保险数据讨论了它们的应用。     

广义线性模型在汽车保险定价的应用

对非寿险产品分类费率的厘定通常采用单项分析法、最小偏差法和多元线性回归等方法。虽然这些方法在非寿险产品定价中仍然占有一度之地,但由于保险数据的特殊性,它们的缺陷越来越受到人们的重视。本文简要分析了这些传统定价方法存在的缺陷,介绍了非寿险精算中典型的广义线性模型,并通过汽车第三者责任保险的损失数据说明了广义线性模型在非寿险产品定价中的具体应用,以及应用广义线性模型时应该注意的几个问题。     

广义η-偏差函数的单调性和不等式

本文讨论了有关广义η-偏差函数的单调性,得到了广义η-偏差函数和其它相关偏差函数的精确不等式．     

广义生灭最小Q过程的可配称性

研究全稳定广义生灭最小Q过程的可配称性,获得广义生灭最小Q过程是可配称的充分必要条件,以及最小Q过程是唯一的可配称Q过程的充分必要条件．     

可卷型L_2-偏差的水平置换方法构造混合水平设计

提出了一种通过置换因子的水平来构造具有较小可卷型L_2-偏差的混合水平均匀设计的新方法.首先建立了混合水平设计的平均可卷型L_2-偏差与广义字长型的定量关系,并以具有较小广义字长型的混合水平设计为初始设计,对其作水平置换,计算其可卷型L_2-偏差,找到具有最小的可卷型L_2-偏差的设计就是相对较好的设计.为了使算法更加有效,还运用了可卷型L_2-偏差的两个性质.数值结果显示通过这种方法构造的设计在可卷型L_2-偏差下表现良好.     

求解大型非对称线性方程组的不完全广义最小向后扰动法

本文给出了求解大型非对称线性方程组的广义最小向后扰动法(GMBACK)的截断版本——不完全广义最小向后扰动法(IGMBACK).该方法基于Krylov向量的不完全正交化,从而在Krylov子空间上求出一个近似的或者拟最小向后扰动解.本文对新算法IGMBACK做了一些理论研究,包括算法的有限终止、解的存在性和唯一性等方面的研究;且给出了IGMBACK的执行.数值实验表明:IGMBACK通常比GMBACK和广义最小残量法(GMRES)更有效;且IGMBACK和GMBACK经常比GMRES收敛得更好.特殊地,如果系数矩阵是敏感矩阵,且方程组右侧的向量平行于系数矩阵的最小奇异值对应的左奇异向量时,重新开始的GMRES不一定收敛,而IGMBACK和GMBACK一般收敛,且比GMRES收敛得更好.     

广义偏最小二乘(PLS)估计类

本文研究了一类含有偏最小二乘（partialleastsquaresPLS）估计的估计类．给出了PLS估计的一般代数形式；讨论了含有PLS估计的广义PPLS估计类的统计性质；给出了该估计类优于最小二乘估计的条件．     

半无限体压入的矢量分析与广义积分问题

本文提出一种以矢量分析建立平冲头压入半无限体连续速度场的方法,首次采用广义积分与参变量积分对该速度场求得冲头压力上界解析解。该解以待定参数法确定的最小上界值为n_j=2.85,与三角形速度场最小上界值误差仅为0.7%。     

带线性约束的线性模型中回归系数的平衡LS估计

基于平衡损失的思想和最小二乘统一理论,对带线性约束的一般线性模型提出了一种全面度量估计优良性的标准．给出了此标准下模型中回归系数线性函数的约束广义平衡LS估计,并得到了约束广义平衡LS估计唯一性的一个充分条件．     

层次分析中的似然函数排序法

给出了层次分析中的似然函数排序法,这一算法不需要一致性检验,而且简明实用.     

α-非有丝分离集的存在性

<正> 近年来自然数域上的递归论已向广义递归论上发展.广义递归论的一个分支是研究在序数的可允许前节α上的递归论,人们通常称之为α-递归论α目前α-递归论的大多数成果是探讨哪些自然数域上递归论的结果可以推广到α-递归论上去.已有的结果表明,有些自然数域上递归论的结果可以推广到序数的一切可允许前节上去.例如Post问     

广义模糊一致矩阵的性质及指数排序方法

在模糊一致矩阵的基础上,引入了广义模糊一致矩阵.研究了广义模糊一致矩阵的性质及指数排序方法,给出了判定模糊矩阵是广义模糊一致矩阵的充要条件,得到了广义模糊一致矩阵的求排序向量的指数排序方法,以及该指数排序方法是强条件下保序的结论.     

Inferring consensus weights from pairwise comparison matrices without suitable properties

Pairwise comparison is a popular method for establishing the relative importance of n objects. Its main purpose is to get a set of weights (priority vector) associated with the objects. When the information gathered from the decision maker does not verify some rational properties, it is not easy to search the priority vector. Goal programming is a flexible tool for addressing this type of problem. In this paper, we focus on a group decision-making scenario. Thus, we analyze different methodologies for getting a collective priority vector. The first method is to aggregate general pairwise comparison matrices (i.e., matrices without suitable properties) and then get the priority vector from the consensus matrix. The second method proposes to get the collective priority vector by formulating an optimization problem without determining the consensus pairwise comparison matrix beforehand.     

A cosine maximization method for the priority vector derivation in AHP

The derivation of a priority vector from a pair-wise comparison matrix (PCM) is an important issue in the Analytic Hierarchy Process (AHP). The existing methods for the priority vector derivation from PCM include eigenvector method (EV), weighted least squares method (WLS), additive normalization method (AN), logarithmic least squares method (LLS), etc. The derived priority vector should be as similar to each column vector of the PCM as possible if a pair-wise comparison matrix (PCM) is not perfectly consistent. Therefore, a cosine maximization method (CM) based on similarity measure is proposed, which maximizes the sum of the cosine of the angle between the priority vector and each column vector of a PCM. An optimization model for the CM is proposed to derive the reliable priority vector. Using three numerical examples, the CM is compared with the other prioritization methods based on two performance evaluation criteria: Euclidean distance and minimum violation. The results show that the CM is flexible and efficient.     

Asymptotic loss of priority scheduling policies in closed re-entrant lines: A computational study

In this paper we present an approximate but efficient analytical method to compute the asymptotic loss of buffer priority scheduling policies in closed re-entrant lines. For simple two-station closed re-entrant lines, this enables the verification of Harrison-Wein conjectures and Jin-Ou-Kumar resuts. For multi-station re-entrant lines, this provides an efficient way of comparing different buffer priority scheduling policies. We also use the method to evaluate the effect of high priority jobs in re-entrant lines.     

Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.     

Analysis of nonpreemptive priority queues with multiple servers and two priority classes

In this paper, we model a priority multiserver queueing system with two priority classes. A high priority customer has nonpreemptive priority over low priority customers. The approaches for solving the problem are the state-reduction based variant of Kao, the modified boundary algorithm of Latouche, the logarithmic reduction algorithm of Latouche and Ramaswami, and the power-series method of Blanc. The objectives of this paper are to present a power-series implementation for the priority queue and to evaluate the relative efficiencies of alternative procedures to compute various performance characteristics. In the paper, we find that at times the logarithmic reduction algorithm may not perform as well as expected and the power-series approach can occasionally pose numerical difficulties.     

基于优先关系的多指标区间决策

讨论了指标的权重和效用值都包含区间数的多指标决策问题,构造了方案优先关系的和谐性检验和不和谐性检验,给出了方案间的“强优”和“弱优”关系,研究了方案排序的原理和方法.     

多属性决策的群排序方法研究

在多属性群决策中，决策的决策结果有两种表现形式，即决策方案的优先序和决策方案的排序权向量。本研究基于决策方案优先序的群排序方法，提出了加权偏差平方和最小化方法及基于测度函数的0-1规划方法。