综合评价中异常值的识别及无量纲化处理方法

针对综合评价中的异常值现象,讨论了原始数据中是否存在异常值、若存在异常值该如何识别异常值以及对含有异常值的评价数据如何进行无量纲化处理三个问题。关于异常值的判断与识别,给出了以“中位数”为参考点,通过比较排序后两端数据偏离中位数的距离的处理思路。对含有异常值的评价数据的无量纲化处理问题,基于常用的“极值处理法”,通过分别指定异常值和非异常值无量纲化取值区间的方式,提出了一种分段的无量纲化处理方法。最后,通过与已有文献异常值识别及无量纲化处理结果的对比分析,验证了本文方法的有效性,发现本文给出的方法能够实现对异常值的适度筛选,且能够提升无量纲化数据分布均衡性。     

充电桩运行异常率预测

充电桩运行是否稳定关系到电动车充电网络整体运行效率。对充电桩运行的异常率进行预测,可以为运营部门提前做好运营决策。本文使用冀北地区电动车充电网络运行数据,针对充电桩异常记录的特征,将广义AR(q)模型和回归模型进行组合,对冀北地区电动汽车充电网络的异常率进行了预测,预测绝对误差平均在0.0044,得到了可以接受的预测效果。     

高频监测数据的异常数据特征分析与检测

对某火电厂10个设备的60000条高频监测数据进行基本的统计分析,得出高频数据的异常数据具有异常点和异常段两种特征,提出了一种基于频数分布和一阶向前差分的检测高频数据中的异常点和异常段的方法.根据数据的一阶向前差分绝对值的频率分布以及风险系数来确定异常数据的阈值,根据设备本身的性能和采样频率确定了异常段所包含异常点的最大个数,根据阈值和最大异常点个数给出异常点和异常段的判断规则.用该方法诊断火电厂前置泵电机的6000条数据的异常数据,结果与实际异常数据相符.     

广义空间异常值修正模型及应用

本文针对广义空间模型中出现的异常值提出了相应的修正模型。分别基于广义空间模型的均值扰动和方差扰动下提出了对应的异常值修正模型,并给出了参数估计方法。通过将该方法应用于中国能源利用效率的区域特征问题,分析了其中的异常值并建立了异常值修正模型。该修正模型能有效改进模型拟合效果,为处理数据中出现的异常值提供了一种新的思路。     

用滑动平均法确定地球化学异常

由于地球本身的不均匀性以及各种因素的影响,造成了地壳上化学元素的含量在空间上分布的不均匀性·这种不均匀性称为地球化学的分散模式,查明元素的地球化学模式是地球化学工作者的任务,一般根据找矿的需要,把地球化学分散模式分为背景模式与异常模式. 背景模式,在某种地球物质中某一元素的“正常”含量的变化称为背景,它是一个含量范围,而不是一个数值. 地球化学异常是在一定的范围内的对正常地球化学背景模式的偏离.一般认为超过背景含量的1.5或2倍标准离差的数值即属于异常. 地球化学探矿就是寻找地球化学中的异常模式,通过异常的线索来…     

指数分布场合异常数据的检验

本文讨论了指数分布场合异常数据的检验，当数据中同时含有异常大、异常小的数据时给出了检验方法，导出了检验统计量及其近似分布，用一个例子说明了所给方法。     

ATM交易状态特征分析与异常检测

考虑ATM交易过程当中产生的一系列参数,如交易量、交易成功率和响应时间等,对交易状态特征进行分析并建立了异常检测模型。针对成功率与响应时间2个参数,利用聚类算法将数据点划分为正常点、疑似异常点、异常点3大类。对于疑似的异常点,再根据其时间序列周围点的分布情况确定是否确实为异常点;对于交易量参数,首先通过LOF局部离群因子对离群点进行识别,再结合交易量随时间的移动均线及标准差加以辅助筛选,得到初步的疑似异常点,进一步通过与不同天同一时刻数据进行比较,最终确定是否为异常点。根据上述模型,本文将异常情况划分为3个预警等级,并对重大故障情况进行预测。     

统计控制图的异常判断准则分析

本篇文章在介绍统计控制图原理基础上,列举了常规控制图现有的异常判断准则,并指出现有准则存在的缺点。提出了对三个问题的异常判断准则分析。并推导出这三个问题异常判断准则的一般性结论。     

应用RJMCMC方法识别线性模型异常点

传统线性模型异常点识别方法容易发生误判:正常点被归为异常点或者异常点被归为正常点.为解决此类问题,提出了应用逆跳马尔科夫蒙特卡洛方法识别异常点的思想,同时将其应用于实际数据加以检验,识别效果明显好于传统方法.     

基于流特性的网络流量异常检测研究

随着因特网规模的不断扩大和复杂化,各种异常行为频繁发生.有效地检测出网络中的流量异常行为,对于保证网络正常运行具有很重要的意义.文章提出了一种根据非饱和链路中的流特性的网络流量异常检测算法.该算法综合利用了指数加权移动平均(exponentially weighted moving average,EWMA)预测模型检测突变异常和均衡模型(equilibrium model,EQM)检测相关性流异常的能力,对链路流量进行建模,检测链路中流量异常.实验结果分析表明:对比于其他检测算法,文章提出的方法能够有效地检测多类异常,并具有很好的检测效果.     

Functional outlier detection with robust functional principal component analysis

Functional principal component analysis is the preliminary step to represent the data in a lower dimensional space and to capture the main modes of variability of the data by means of small number of components which are linear combinations of original variables. Sensitivity of the variance and the covariance functions to irregular observations make this method vulnerable to outliers and may not capture the variation of the regular observations. In this study, we propose a robust functional principal component analysis to find the linear combinations of the original variables that contain most of the information, even if there are outliers and to flag functional outliers. We demonstrate the performance of the proposed method on an extensive simulation study and two datasets from chemometrics and environment.     

Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions

We consider the problems of robust estimation and testing for a log-linear model with feedback for the analysis of count time series. We study inference for contaminated data with transient shifts, level shifts and additive outliers. It turns out that the case of additive outliers deserves special attention. We propose a robust method for estimating the regression coefficients in the presence of interventions. The resulting robust estimators are asymptotically normally distributed under some regularity conditions. A robust score type test statistic is also examined. The methodology is applied to real and simulated data.     

基于极值理论的EXPAR时序模型异常点诊断

本文基于极值理论给出诊断EXPAR模型异常点检验统计量的渐近分布,并依此渐近分布来选取检验的临界值。这种方法选取的临界值可保证控制在一定显著性水平下,而且可以计算渐近p值,比仿真选取的临界值更科学合理。     

Some remarks to multivariate regression model

Some remarks to problems of point and interval estimation, testing and problems of outliers are presented in the case of multivariate regression model. This work was supported by the Council of Czech Government J14/98:153100011.     

极值分布和威布尔分布异常数据的检验方法

本文对威布尔分布的极值分布异常数据的检验给出了一系列的方法，首先，导入了极值分布下一般Ｄｉｘｏｎ型统计量的精确分布，同时还给出了改进的Ｇ型统计量，及它们的分位点表。最后本文提出了一个新的统计量；Ｆ型统计量，并用Ｍｏｎｔｅ－Ｃａｒｌｏ模拟的方法给出其分位点表，从而首次给出威布尔分布异常值的直接检验方法。本文进一步讨论了这些检验方法的功效，且表明Ｆ型检验是最优的。     

The distribution of the maximum likelihood-ratio statistic used for testing a normal sample for three outliers

We study the exact distribution of the likelihood-ratio statistic used for testing a normal sample for three upper (lower) outliers. We obtain recursive correlations for the integral distribution function of this statistic. We apply the obtained correlations for calculating critical values of the likelihood-ratio statistic which appear to be close to critical values of this statistic simulated by the Monte Carlo method. We give an example of the joint use of the likelihood-ratio statistic for testing a sample for more than one outlier.     

Detecting multiple outliers in linear regression using a cluster method combined with graphical visualization

This paper provides a graphical visualization of multiple outliers based on a clustering algorithm using the minimal spanning tree, and proposes a modified version of this clustering algorithm for identifying multiple outliers. Graphical visualization is helpful for the classification of multiple outliers. It is shown that the proposed modified procedure preserves the performance of the clustering algorithm in identifying multiple outliers, but also reduces the problem of swamping of observations.     

Choosing a Linear Model with a Random Number of Change-Points and Outliers

The problem of determining a normal linear model with possible perturbations, viz. change-points and outliers, is formulated as a problem of testing multiple hypotheses, and a Bayes invariant optimal multi-decision procedure is provided for detecting at most k (k > 1) such perturbations. The asymptotic form of the procedure is a penalized log-likelihood procedure which does not depend on the loss function nor on the prior distribution of the shifts under fairly mild assumptions. The term which penalizes too large a number of changes (or outliers) arises mainly from realistic assumptions about their occurrence. It is different from the term which appears in Akaikes or Schwarz criteria, although it is of the same order as the latter. Some concrete numerical examples are analyzed.     

Multiple outlier detection in growth curve model with unstructured covariance matrix

Under a normal assumption, Liski (1991,Biometrics,47, 659–668) gave some measurements for assessing influential observations in a Growth Curve Model (GCM) with a known covariance. For the GCM with an arbitrary (p.d.) covariance structure, known as unstructured covariance matrix (UCM), the problems of detecting multiple outliers are discussed in this paper. When a multivariate normal error is assumed, the MLEs of the parameters in the Multiple-Individual-Deletion model (MIDM) and the Mean-Shift-Regression model (MSRM) are derived, respectively. In order to detect multiple outliers in the GCM with UCM, the likelihood ratio testing statistic in MSRM is established and its null distribution is derived. For illustration, two numerical examples are discussed, which shows that the criteria presented in this paper are useful in practice.Supported partially by the WAI TAK Investment and Loan Company Ltd. Research Scholarship of Hong Kong for 1992–93.Supported partially by the Hong Kong UPGC Grant.