四分位数及迭代稳健统计法在实验室能力验证中的应用

以X年能力验证数据为例,用四分位数和迭代稳健统计法分别进行统计比较。结果表明迭代稳健统计的标准差较四分位数稳健统计大,不合格实验室数较四分位数稳健统计少。以剔除离群值后的经典统计法结果作为依据,对两种方法合理性进行判断。相对于四分位数稳健统计,迭代稳健统计的结果更接近于经典统计法,是一种更可靠的统计方法。     

稳健统计技术在校准实验室能力验证中的应用

为确保CNAS对实验室校准项目认可的有效性,实验室除进行纵向溯源外,还应在认可周期内进行横向能力验证活动。文章主要介绍校准实验室的能力验证判别方法,尤其介绍以稳健统计量Z比分数为判据的稳健统计技术在能力验证活动中的应用。     

基于稳健统计分析的全站仪比对方法

为较好地实现全站仪加常数和乘常数校准结果的实验室比对,本文提出了采用六段比较法和六段解析法分别校准的方案并依据稳健统计量作为判断准则。各参比实验室比对结果和稳健统计结果表明,所述比对方法具有实用参考价值。     

实验室间比对的能力验证及稳健统计技术第七讲稳健统计技术(三)

第六讲介绍了各种表格在能力验证稳健统计技术中的应用。本讲将介绍各种图形在稳健统计技术中的应用以及根据统计结果,如何评价和分析参加实验室的检测能力,最后叙述能力验证结果的利用。     

能力验证结果统计处理中几种改进的稳健算法

在检测能力验证计划中,为了统计评价参加实验室的结果,通常采用稳健算法。本文简要介绍了ISO13528：2005附录C中的稳健算法A,并指出其局限性,最后提出了几种改进的稳健算法。     

稳健统计方法在实验室能力验证中的应用

统计方法稳健性的重要意义已为广大专家学者认同,稳健统计学作为数理统计的一个分支,在质量管理工作中发挥着重要作用.现今,统计方法的稳健性问题已得到专家学者的重视,在经典统计法的应用条件不能严格满足时,稳健统计方法的优势便展现出来.实验室能力验证作为质量管理工作的重要组成部分,也得到其强力支持.常用的稳健统计方法有四分位法...     

应用稳健统计法对实验室间比对实验结果评价

采用稳健统计法统计,以直方图、正态分布图和尤登图对统计结果进行表示,对实验室间比对实验结果进行结果评价。统计结果表明,采取的统计分析是有效的、合理的,并有助于离群实验室发现问题,找出自身差距,进行整改。     

简析稳健统计技术

讲述稳健统计技术的基本参数和其对实验室能力结果的评价方法,简单举例说明数据分析的方法与环节。     

EXCEL在稳健（Robust）统计技术中的应用

稳健统方法（Robust统计）是用中位值和标准化4分位距IQR值代替平均值和标准偏差,避免了极端值（离群值）对统计结果的影响,从而可以比较客观、科学地评价每一个实验室的结果。本文给出了稳健统计的基本原理和用Excel处理数据的操作方法,使大量繁琐的计算自动化,统计结果更准确、简便、快捷。     

稳健统计方法在物理拉伸实验室内部比对的应用

依据《能力验证结果的统计处理和能力评价指南》CANS—GL02，采用四分位数稳健统计方法对物理拉伸实验室内部比对进行统计，和采用传统计Z比方法统计出来的结果比较分析。     

稳健统计与格拉布斯准则在能力验证结果分析中的应用

分别运用稳健统计（四分位和迭代）法、格拉布斯准则剔除离群值后用经典统计法对热轧带肋钢筋的6组数据进行了统计分析。结果表明:数据组中Z比分值大于5的数据,为离群值。数据组总体服从正态分布或接近正态分布的,3种统计方法结果基本吻合;不存在离群数据的情况下,建议采用稳健统计（四分位或迭代）法进行统计;存在离群数据的情况下,应采用格拉布斯准则剔除离群值后用经典统计法进行统计。对于数据明显偏离正态分布的,不存在离群数据的情况下,建议采用迭代稳健统计法进行统计;存在离群数据的情况下,应采用格拉布斯准则剔除离群值后用经典统计法进行统计。     

Multiframe phase-diversity algorithm for active imaging

A multiframe phase-diversity algorithm for imaging through the turbulent atmosphere tailored to the statistics of coherent light is developed and presented. The problem is posed as a maximum likelihood estimation where pupil-plane intensity data and atmospheric statistics are used to regularize the inverse problem. Reconstruction results characterized by residual mean square error are presented for varying detection parameters. The resulting algorithm appears to be robust under detection noise processes and results in significant improvement of processed images.     

实验室间比对结果分析统计方法的探讨

对实验室间比对结果分析的统计方法进行了探讨。比较了常用的统计方法——稳健统计法和经典统计法的计算过程，对几次实验室间的比对结果分别用这两种统计方法进行了分析，同时结合格拉布斯检验法对离群值进行判断，从而探讨了两种统计方法的适用范围。认为当比对数据较多时，可采用稳健统计法给出严格的评价结论；当比对数据较少时，应先利用格拉布斯检验法对离群值进行剔除，同时要考虑方法标准或产品标准规定的允许误差，然后对余下的数据用经典统计法进行分析和评价。     

Mathematical Theory of Probability and Statistics

Two test statistics are suggested for discriminating between the exponential model and the more general Weibull or gamma models, and these are compared to some previously used test statistics by Monte Carlo methods. The results of estimating reliability under an exponential assumption when the true model is Weibull is also investigated. These results as well as the tests mentioned above indicate that the exponential model is often not adequate when the more general models hold. In contrast to this result it was found that the Weibull model was quite robust relative to the generalized gamma distribution with regard to reliability estimation. Some general pivotal function properties are presented for the maximum likelihood estimator of reliability for the generalized gamma distribution and similar results also hold for the Weibull procedure under a generalized gamma assumption. These results made a Monte Carlo study of this problem feasible. Since the maximum likelihood estimators are apparently ill-behaved for smaller sample sizes and since the Weibull model is robust it appears little is gained by using the generalized gamma distribution for samples of size less than 200 to 400.     

GLM profile monitoring using robust estimators

It is well known that performance of control scheme in phase II of statistical process control depends on the estimators utilized in phase I. Sometimes, outliers may be present in the data, which could seriously impact the performance of the estimators. In some practical situations, generalized linear models (GLMs) are used to model a wide class of response variables. This study deals with the robust estimation and monitoring of parameters in GLM profiles in the presence of outliers. In this study, robust estimators are used to estimate the parameters of logistic and Poisson profiles. The results are compared with the maximum likelihood estimators (MLEs). In a numerical example, the profile parameters are estimated by the MLE and robust estimators and the resulting test statistics are monitored by a control scheme. The phase II control charts are determined based on these two types of estimators and compared for different out-of-control conditions. The simulation results confirm that robust estimators in most cases lead to better estimates in comparison with the MLE estimator in terms of average run length criterion.     

FMS 中加工质量检测与控制技术的研究

 介绍了一种新的变携式电子仪器用以采集加工质量数据。通过鲁棒统计,提出了一个鲁棒估计算法处理测量结果和误差。应用模糊集理论提出一个新的模糊逻辑控制器并实现了加工质量模糊自动控制。     

An Adaptive M-Estimator and Its Application to a Selection Problem

A robust adaptive estimation procedure for location estimation problems is developed. Classification in this procedure is done on the basis of skewness and tailweight, using two statistics that are ratios of linear functions of sample order statistics. The associated estimators are of the general type known as M-estimators, Following the development of the adaptive location estimation procedure, an application to the k population selection problem is given. Monte Carlo results show the superiority of the adaptive procedure to the sample means procedure, the rank sum procedure, and the previously developed adaptive procedure of Randles, Ramberg, and Hogg.     

Comparisons of Shewhart-type rank based control charts for monitoring location parameters of univariate processes

The nonparametric (distribution-free) control charts are robust alternatives to the conventional parametric control charts when the form of underlying process distribution is unknown or complicated. In this paper, we consider two new nonparametric control charts based on the Hogg–Fisher–Randle (HFR) statistic and the Savage rank statistic. These are popular statistics for testing location shifts, especially in right-skewed densities. Nevertheless, the control charts based on these statistics are not studied in quality control literature. In the current context, we study phase-II Shewhart-type charts based on the HFR and Savage statistics. We compare these charts with the Wilcoxon rank-sum chart in terms of false alarm rate, out-of-control average run-length and other run length properties. Implementation procedures and some illustrations of these charts are also provided. Numerical results based on Monte Carlo analysis show that the new charts are superior to the Wilcoxon rank-sum chart for a class of non-normal distributions in detecting location shift. New charts also provide better control over false alarm when reference sample size is small.     

Robust and exploratory data analysis techniques for modelling materials and process variables in welding

Abstract

The modelling and quantification of materials data using numerically optimised mathematical functions is an important and widespread practice. In the present paper the development of robust and exploratory data analysis strategies, based on exploratory data analysis procedures and robust statistics, is described. Application of this new PC based data analysis system to two sets of established data which relate materials information to process variables demonstrates the advantages of a comprehensive and robust approach to analysis. The advantages of techniques that use both ‘degree off it’ and also predictive statistical criteria for mathematical/statistical models are described.

MST/1945     

Robust asymptotic tests for the equality of multivariate coefficients of variation

In order to easily compare several populations on the basis of more than one feature, multivariate coefficients of variation (MCV) may be used as they allow to summarize relative dispersion in a single index. However, up to date, no test of equality of one or more MCVs has been developed in the literature. In this paper, several classical and robust Wald-type tests are proposed and studied. The asymptotic distributions of the test statistics are derived under elliptical symmetry, and the asymptotic efficiency of the robust versions is compared to the classical tests. Robustness of the proposed procedures is examined through partial and joint influence functions of the test statistic, as well as by means of power and level influence functions. A simulation study compares the performance of the classical and robust tests under uncontaminated and contaminated schemes, and the difference with the usual covariance homogeneity test is highlighted. As a by-product, these tests may also be considered in the univariate context where they yield procedures that are both robust and easy-to-use. They provide an interesting alternative to the numerous parametric tests existing in the literature, which are, in most cases, unreliable in presence of outliers. The methods are illustrated on a real data set.