大数据下基于体积抽样的异常点诊断及估计问题

处理大规模数据集时,抽样是一种很受欢迎的有效方法。体积抽样作为一种联合抽样的方法,它是按照与矩阵平方的行列式成比例进行抽样。该方法在线性回归模型背景下能得到参数的无偏估计。然而也容易受到异常点的影响,本文感兴趣的是体积抽样受异常点影响的程度。基于数据删除模型和均值漂移模型构建统计量进行异常点诊断,结果发现体积抽样方法在某些情况下极易受异常点影响。但是在给定损失的条件下,比独立同分布抽样所需的子样本量更小,在此基础上,提出样本量的自适应选择方法。作为体积抽样的扩展,杠杆值体积抽样同样可以得到普通最小二乘线性模型参数的无偏估计,一个有趣的发现是使用杠杆值体积抽样,等权最小二乘估计结果比非等权最小二乘估计效果好。     

一类基于秩次的稳健线性回归估计与诊断方法

线性回归模型的误差项不服从正态分布或存在多个离群点时,可以将残差秩次的某些函数作为权重引入估计模型来减少离群点的不良影响。本文从参数估计、稳健性质、回归诊断等方面对基于残差秩次的一类稳健回归方法进行介绍.通过模拟研究和实例分析表明,R和GR估计是一种估计效率较高的稳健回归方法,其中GR估计可同时避免X与Y空间离群点,而高失效点HBR估计可通过控制某个参数在稳健性与估计效率之间进行折衷.     

半参数回归模型中随机加权M估计的强逼近

用随机加权法给出了半参数回归模型中参数的随机加权M估计，在一般的条件下证明了用随机加权统计量的分布逼近原估计量误差的分布的强有效性，并给出了M估计的最优强收敛速度。     

非参数模型均值函数结构变点的Bootstrap检测

本文检测非参数回归模型均值函数结构变点,针对均值函数跃度的长期均值为零时,基于残量的CUSUM统计量对均值函数结构变点检验无效的问题,本文提出了一种基于均值函数的核估计的检验统计量,得到统计量在原假设和备择假设下的极限分布,并构造Bootstrap方法对非参数回归模型均值函数结构变点进行检验,证明了检验和估计的一致性;模拟结果表明本文方法明显优于已有方法。     

随机设计下非参数回归模型方差变点Ratio检验

研究随机设计下非参数回归模型方差变点Ratio检验.首先用局部多项式方法估计回归曲线得到残差序列,其次基于残差的平方序列构造Ratio检验统计量并推导检验统计量的极限分布.最后数值模拟与实例分析结果表明方法的有效性.     

固定设计点的半参数回归模型的M估计的渐进分布

讨论了固定设计点的半参数回归模型的大样本性质,得到了模型的Ｍ估计的渐进分布,并建立了关于参数的检验。数值模拟结果表明,本文提出的方法具有较好的稳健性。     

AR模型识别及其参数的高阶Yule—Walker估计

一、引言自回归谱估计在许多领域获得了广泛的应用,随着科学技术的不断发展,对谱估计分辨力的要求越来越高.高阶 Yule-Walker 谱估计就是具有较高分辨力的一种估计(见[1]).为了进一步研究这种估计首先必须研究自回归模型阶的识别及其参数的高阶Yule-Walker 估计.通常使用的自回归定阶方法与参数的矩估计方法使用了样本自协方差函数(或样本自相关函数)在零点的值(?)(0)(或(?)(0))。而(?)(0)(或(?)(0))就是影响自回归谱估计分辨力的一个重要因素.实际上,通常的信号观测值包含两部分     

随机权函数半参数回归模型的导方差统计分析

在回归分析中,观测值的方差齐性只是一个基本的假定,在参数、半参数和非参数回归模型中关于异方差检验和估计问题已有很多研究.本文在冉昊和朱忠义(2004)讨论的半参数回归模型的基础上,用随机参数方法,讨论随机权函数半参数回归模型中的异方差检验问题,得到了方差齐性检验Score统计量,同时,当半参数模型存在异方差时,本文还给出了估计方差的方法.     

混合效应模型中的D-最优设计

本研究的是混合效应-Ⅱ模型，首先，给出二阶模型存在D-最优回归设计点的必要条件。其次，组合D-最优回归设计和D-最优区组设计BIBD构造D-最优设计并给出寻找新的D-最优设计点的方法。以两种有代表性的组合误差分布为例，阐明新的D-最优设计点的获得过程，并给出了数值结果。     

非参数固定设计回归模型中的Stahel-Donoho核估计

研究非参数固定设计回归模型中的稳健核估计. 提出了一种Stahel-Donoho核估计, 在此核估计中, 权重函数既依赖于数据深度, 又依赖于设计点和估计点之间的距离. 对不可直接计算的误差深度, 利用局部近似, 给出了一种近似计算方法, 使得新的估计是计算有效的. 新的估计获得较高的崩溃点值, 并有渐近正态和均方收敛等良好的大样本性质. 与参数模型中的深度加权估计不同的是,这种深度加权非参数估计有简单的方差结构,于是,人们可以比较新旧估计的有效性.数据模拟结果表明,新的方法可以平滑回归估计,并获得稳健性和有效性的良好平衡.     

龚帕兹曲线参数估计方法及应用研究

在现有文献研究的基础上,对龚帕兹曲线参数估计问题又作了进一步研究,给出了求解龚帕兹曲线参数的一种新方法.将最优化方法与回归方法结合在一起,利用最优化理论中的区间搜索和一维搜索得到一系列c值,利用回归方法可求得与其相对应的一系列a和b的值,当c取最优c*时,a和b便得到最优值a*和b*.经示例计算表明,这种求解龚帕兹曲线参数的方法在电力系统负荷预测中具有较高的预测精度.     

Logistic回归模型在信用风险分析中的应用

通过运行SPSS,建立L og istic回归信用评价模型(cred it eva luation m odel),用来对中国2000年106家上市公司进行两类模式分类,这两类模式是指按照公司的经营状况分为“差”和“正常”两个小组.对每一家上市公司,考虑其经营状况的4个主要财务指标:每股收益、每股净资产、净资产收益率和每股现金流量.仿真结果表明,L og istic回归信用评价模型对总体106个样本,判别准确率达到99.06%.此外,本文的研究结果还发现,当利用SPSS的D iscrim inan t给出的模型系数建立的线性判别分析模型和利用SPSS的M u ltinom ia lL og istic给出的模型参数建立的L og istic回归模型,L og istic回归模型的判别结果不如线性判别模型.但如果剔除不合格的样本,或是将样本数据规格化,则可以提高L og istic回归模型的分类准确率.     

Support vector quantile regression with varying coefficients

Quantile regression has received a great deal of attention as an important tool for modeling statistical quantities of interest other than the conditional mean. Varying coefficient models are widely used to explore dynamic patterns among popular models available to avoid the curse of dimensionality. We propose a support vector quantile regression model with varying coefficients and its two estimation methods. One uses the quadratic programming, and the other uses the iteratively reweighted least squares procedure. The proposed method can be applied easily and effectively to estimating the nonlinear regression quantiles depending on the high-dimensional vector of smoothing variables. We also present the model selection method that employs generalized cross validation and generalized approximate cross validation techniques for choosing the hyperparameters, which affect the performance of the proposed model. Numerical studies are conducted to illustrate the performance of the proposed model.     

Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms

Logistic regression is a simple and efficient supervised learning algorithm for estimating the probability of an outcome or class variable. In spite of its simplicity, logistic regression has shown very good performance in a range of fields. It is widely accepted in a range of fields because its results are easy to interpret. Fitting the logistic regression model usually involves using the principle of maximum likelihood. The Newton–Raphson algorithm is the most common numerical approach for obtaining the coefficients maximizing the likelihood of the data. This work presents a novel approach for fitting the logistic regression model based on estimation of distribution algorithms (EDAs), a tool for evolutionary computation. EDAs are suitable not only for maximizing the likelihood, but also for maximizing the area under the receiver operating characteristic curve (AUC). Thus, we tackle the logistic regression problem from a double perspective: likelihood-based to calibrate the model and AUC-based to discriminate between the different classes. Under these two objectives of calibration and discrimination, the Pareto front can be obtained in our EDA framework. These fronts are compared with those yielded by a multiobjective EDA recently introduced in the literature.      

A multi-point numerical integrator with trigonometric coefficients for initial value problems with periodic solutions

Based on the collocation technique, we introduced a unifying approach for deriving a family of multi-point numerical integrators with trigonometric coefficients for the numerical solution of periodic initial value problems. A practical 3-point numerical integrator was presented, whose coefficients are generalizations of classical linear multistep methods such that the coefficients are functions of an estimate of the angular frequency ω. The collocation technique yields a continuous method, from which the main and complementary methods are recovered and expressed as a block matrix finite difference formula that integrates a second-order differential equation over non-overlapping intervals without predictors. Some properties of the numerical integrator were investigated and presented. Numerical examples are given to illustrate the accuracy of the method.     

基于大样本电梯抽查数据的风险评估Logistic模型

为了提高在用电梯监督抽查工作效率及有效性,在统计分析G市电梯安全监管抽查的大样本数据基础上,构建以电梯使用情况、电梯基本参数及制造维保相关情况等为指标的管理体系。根据电梯抽查数据的实质,先对数据进行变量筛选,然后构建风险分级,最后对前人的方法作出改进形成风险矩阵法并提出以Logistic方法为电梯整机风险建立量化模型,最终形成电梯整机风险评估体系。从理论的角度看,通过使用LIFT统计量和K-S统计量比较两种风险值计算模型,得出用Logistic方法进行风险分层更为准确。而实际的工程应用表明,利用Logistic回归法与基于平均风险值赋权比例法的组合为电梯安全监管抽样调查提供的筛选比例,比现有的方法更合理准确。     

医学研究中标准曲线修正的初探

目的:在医学研究和临床检验中经常使用定量检测法,在该方法中标准曲线被广泛应用,且其准确性直接影响检测结果.从统计学角度对其进行修正,使结果更为可靠.方法:应用直线回归法对直线型标准曲线进行处理,求出直线回归方程;对曲线型标准曲线,先将指数方程取对数后变成直线方程,然后求直线回归方程后再回代,求出曲线回归方程.结果:以蛋白量与其对应的OD值间关系求得直线回归方程y=0 .0 0 6x;以TNFα浓度与对应的OD值间关系求得指数回归方程y=0 .0 1 5 x0 .732 ,对两者进行F检验,回归效果均极显著.结论:应用回归法可获得定量检测法中常用的直线型及曲线型标准曲线的方程,据此可直接计算出待测样本所需数据,使其更为简便、准确、可靠.     

Selection of a representative value function in robust multiple criteria ranking and choice

We introduce the concept of a representative value function in robust ordinal regression applied to multiple criteria ranking and choice problems. The proposed method can be seen as a new interactive UTA-like procedure, which extends the UTA^GMS and GRIP methods. The preference information supplied by the decision maker (DM) is composed of a partial preorder and intensities of preference on a subset of reference alternatives. Robust ordinal regression builds a set of general additive value functions which are compatible with the preference information, and returns two binary preference relations: necessary and possible. They identify recommendations which are compatible with all or at least one compatible value function, respectively. In this paper, we propose a general framework for selection of a representative value function from among the set of compatibles ones. There are a few targets which build on results of robust ordinal regression, and could be attained by a representative value function. In general, according to the interactively elicited preferences of the DM, the representative value function may emphasize the advantage of some alternatives over the others when all compatible value functions acknowledge this advantage, or reduce the ambiguity in the advantage of some alternatives over the others when some compatible value functions acknowledge an advantage and other ones acknowledge a disadvantage. The basic procedure is refined by few extensions. They enable emphasizing the advantage of alternatives that could be considered as potential best options, accounting for intensities of preference, or obtaining a desired type of the marginal value functions.     

一类二阶常微分方程无穷多点边值问题多个正解的存在性

研究了一类二阶非线性常微分方程无穷多点边值问题多个正解的存在性,运用不动点指数理论及Holder不等式得到了方程至少存在两个正解的若干充分条件,推广和改进了相关文献的结果.     

Acoustic estimation of suspended sediment concentration

In this paper, the acoustic estimation of suspended sediment concentration is discussed and two estimation methods of suspended sediment concentration are presented. The first method is curve fitting method, in which, according to the acoustic backscattering theory we assume that the fitting factor K₁ (r) between the concentration M(r) obtained by acoustic observation and the concentration M₀ ( r) obtained by sampling water is a high order power function of distancer. Using least-square algorithm, we can determine the coefficients of the high order power function by minimizing the difference betweenM( r) and M₀ ( r) in the whole water profile. To the absorption coefficient of sound due to the suspension in water we do not give constraint in the first method. The second method is recursive fitting method, in which we take M₀ ( r) as the conditions of initialization and decision and give rational constraints to some parameters. The recursive process is stable. We analyzed the two methods with a lot of experimental data. The analytical results show that the estimate error of the first method is less than that of the second method and the latter can not only estimate the concentration of suspended sediment but also give the absorption coefficient of sound. Good results have been obtained with the two methods.