改进遗传算法求解TSP问题

提出了一种改进遗传算法求解 TSP.该方法在迭代初期引入不适应度函数作为评价标准 ,结合启发式交叉和边重组交叉算子设计了一种新的交叉算子 ,并对变异后个体进行免疫操作 .此外对操作后群体进行整理 ,删除群体中相同个体 ,得到规模为 N1的中间群体 ,对较优的 N -N 1个个体进行启发式变异 ,并将变异后个体补充进中间群体 ,生成规模为 N的新群体 ,这样保证群体中没有相同个体 ,从而保证群体多样性 .数值结果表明这种改进遗传算法是有效的 .     

群体多指标决策的偏好集结方法

研究多个指标条件下,利用个体决策结果形成群体一致偏好的方法、假设个体有加性效用函数,将个体多指标效用函数表示成单个指标评价函数的加权和,群体指标评价函数表示成个体指标评价函数的加权和.通过协商指标权重、指标评价函数、支付意愿三个参数,成对个体达成双方一致.提出了(n-1)对个体之间达成双方一致,从而得出群体效用函数的决策方法,这种分析框架同样可以扩展到联盟协商一致中.     

多个Panel数据模型回归系数的参数Bootstrap检验

对多个只含有个体效应的Panel数据模型,研究了模型中回归系数向量相等性的假设检验问题,提出了一种参数Bootstrap检验方法.有限样本的数值模拟研究结果表明,提出的检验方法具有良好.的检验功效,且受个体效应方差、误差方差、模型个数、回归系数维数的影响不明显.     

偏正态数据下联合位置与尺度模型的统计诊断

本文研究偏正态数据下联合位置与尺度模型,考虑基于数据删除模型的参数估计和统计诊断,比较删除模型与未删除模型相应统计量之间的差异.首次提出基于联合位置与尺度模型的诊断统计量和局部影响分析.通过模拟研究和实例分析,给出不同的诊断统计量来判别异常点或强影响点,研究结果表明本文提出的理论和方法是有用和有效的.     

单纯形分布模型的统计诊断

该文讨论了单纯形分布广义线性模型和广义非线性模型的影响分析问题, 得到了若干有用的诊断统计量;证明了数据删除模型和均值漂移模型的的等价性定理.同时还研究了该模型的变离差检验,得到了Score检验统计量.最后给出了两个实例, 说明该文方法的应用价值.     

基于Pena距离的双重广义线性模型的统计诊断

本文主要研究双重广义线性模型,考虑基于数据删除模型的参数估计和统计诊断,比较删除模型与未删除模型相应的诊断统计量之间的变化.首次提出基于双重广义线性模型下的Pena距离.通过一些模拟研究以及实例分析,比较不同诊断统计量判别异常点或强影响点的差异,研究结果表明本文提出的理论和方法是行之有效的.     

约束条件下线性模型协方差阵扰动的影响分析

该文研究了协方差阵扰动和数据删除对最佳线性无偏估计(BLUE)的影响问题, 给出了在约束条件下一般线性模型与在约束条件下Gauss-Markov模型及在约束条件下数据删除模型中回归参数β的BLUE之间的关系式. 作者还定义了度量影响大小的广义Cook距离D_V并给出了D_V的两个计算公式.     

基于MM算法的LAD回归的影响分析

基于Hunter and Lange(2000)提出的MM迭代算法,构造了一个代替L1目标函数的新的目标函数Qε(ββk);在此基础上研究了非线性LAD回归影响分析的若干问题.基于新的目标函数和MM迭代算法,证明了LAD回归模型中数据删除模型和均值漂移模型参数估计的等价性定理,并提出了一种新的影响度量.最后,几个数据实例说明了方法的有效性.     

纵向数据半参数Beta回归模型的影响分析

本将随机效应当作是缺失数据,基于Q函数和EM算法并利用P-样条拟合非参数部分,得到了纵向数据半参数Beta回归模型估计方法.基于数据删除模型,我们得到了模型参数部分的广义Cook距离以及非参数部分的广义DFIT.此外,本文还研究了在四种不同扰动情形下模型的局部影响分析,得到了相应的影响矩阵.最后,我们通过两个数值实例验证了所得诊断统计量的有效性.     

非线性均值方差模型的贝叶斯数据删除统计诊断

文章在非线性均值方差模型框架下基于K-L距离研究贝叶斯数据删除影响的统计诊断问题,通过应用Gibbs抽样和MH算法估计贝叶斯数据删除影响诊断统计量.随机模拟研究和红鳟鲑鱼数据的数值例子说明该诊断方法的可行性.     

Case influence diagnostics in the kaplan-meier estimator and the log-rank test

One or few observations can be highly influential on the Kaplan-Meier estimator, and consequently on the log-rank test statistic in comparing two survival functions. In this paper we derive case influence diagnostics for the Kaplan-Meier estimator and the log-rank test. We note that diagnostics in this context is quite different from the regression context where observations are usually assumed to be independent. Simulation studies are done to present some guidelines to determine influential observations deserving special attention. Illustrative examples are also given.     

Efficient Computation of Subset Influence in Regression

Abstract

The detection of influential cases is now accepted as an essential component of regression diagnostics. It is also well established that two or more cases that are individually regarded as noninfluential may act in concert to achieve a high level of joint influence. However, for the majority of data sets it is computationally infeasible to calculate the influence for all subsets of a given size. In this article we address this problem and suggest an algorithm that greatly reduces the computational effort by making use of a sequence of upper bounds on the influence value. These upper bounds are much less costly to evaluate and greatly reduce the number of subsets for which the influence value must be explicitly determined.     

Influence diagnostics in the varying coefficient model with longitudinal data

One or few observations can be highly influential on estimates of regression coefficients in the linear regression model. In this paper we derive influence diagnostics for the varying coefficients model with longitudinal data. We note that diagnostics in this context is quite different from the classical regression model in the sense that regression coefficients vary as time varies. A version of Cook’s distance is suggested to reflect this specific aspect of varying coefficient model. An algorithm to present some guidelines to determine influential observations deserving special attention is developed. An illustrative example based on the AIDS data is also given.     

Local influence on the goodness-of-fit test statistic in maximum likelihood factor analysis

The influence of observations on the goodness-of-fit test in maximum likelihood factor analysis is investigated by using the local influence method. Under an appropriate perturbation the test statistic forms a surface. One of main diagnostics is the maximum slope of the perturbed surface, the other is the direction vector corresponding to the curvature. These influence measures provide the information about jointly influential observations as well as individually influential observations.     

Influence diagnostics and outlier tests for varying coefficient mixed models

In this paper, we consider subset deletion diagnostics for fixed effects (coefficient functions), random effects and one variance component in varying coefficient mixed models (VCMMs). Some simple updated formulas are obtained, and based on which, Cook’s distance, joint influence and conditional influence are also investigated. Besides, since mean shift outlier models (MSOMs) are also efficient to detect outliers, we establish an equivalence between deletion models and MSOMs, which is not only suitable for fixed effects but also for random effects, and test statistics for outliers are then constructed. As a byproduct, we obtain the nonparametric “delete = replace” identity. Our influence diagnostics methods are illustrated through a simulated example and a real data set.     

Influence Diagnostics in the Common Canonical Variates Model

As a generalization of the canonical correlation analysis to k random vectors, the common canonical variates model was recently proposed based on the assumption that the canonical variates have the same coefficients in all k sets of variables, and is applicable to many cases. In this article, we apply the local influence method in this model to study the impact of minor perturbations of data. The method is non-standard because of the restrictions imposed on the coefficients. Besides investigating the joint local influence of the observations, we also obtain the elliptical norm of the empirical influence function as a special case of local influence diagnostics. Based on the proposed diagnostics, we find that the results of common canonical variates analysis for the female water striders data set is largely affected by omitting just one single observation.     

Influence Diagnostics in Common Principal Components Analysis

In principal components analysis, the influence function and local influence approaches have been well established as important diagnostic tools. In this article, we first review the generalized local influence approach in the restricted likelihood framework. We then apply the restricted likelihood local influence diagnostic in the common principal components analysis. One special part of this local influence result is an elliptical norm of the empirical influence function, which is comparable to the deletion diagnostic scaled by the same matrix which requires iterative solutions for parameter estimates with every case deleted. Local influence diagnostics are constructed by some basic building blocks that are obtained directly from the maximum likelihood estimates of the parameters, and which are based on the original data and thus require less computation. A numerical example illustrates the technique and some joint influence effects are identified by the proposed method.     

线性回归诊断的若干问题

本文对于线性回归诊断提出了几种新的模型和方法。我们首次研究了方差加权和均值漂移的混合模型,得到了相应的诊断统计量。本文还引入了罚函数方法,并以此为工具,讨论了若干有偏估计的影响度量,最后,本文提出了基于重心的诊断统计量,对于识别异常点有较好的效果。     

An influence statistic for linear measurement error models

Detection of multiple outliers or subset of influential points has been rarely considered in the linear measurement error models. In this paper a new influence statistic for one or a set of observations is generalized and characterized based on the corrected likelihood in the linear measurement error models. This influence statistic can be expressed in terms of the residuals and the leverages of linear measurement error regression. Unlike Cook’s statistic, this new measure of influence has asymptotically normal distribution and is able to detect a subset of high leverage outliers which is not identified by Cook’s statistic. As an illustrative example, simulation studies and a real data set are analysed.     

Diagnostic Plots for Robust Multivariate Methods

Robust techniques for multivariate statistical methods—such as principal component analysis, canonical correlation analysis, and factor analysis—have been recently constructed. In contrast to the classical approach, these robust techniques are able to resist the effect of outliers. However, there does not yet exist a graphical tool to identify in a comprehensive way the data points that do not obey the model assumptions. Our goal is to construct such graphics based on empirical influence functions. These graphics not only detect the influential points but also classify the observations according to their robust distances. In this way the observations are divided into four different classes which are regular points, nonoutlying influential points, influential outliers, and noninfluential outliers. We thus gain additional insight in the data by detecting different types of deviating observations. Some real data examples will be given to show how these plots can be used in practice.