稀土分离联产品质量控制研究

以稀土分离企业为背景,抽取联产品特点及质量属性,绘制单一产品的指数加权移动平均控制图和联产品的多元残差T²控制图,并将两类控制图进行对比分析,分析表明和EWMA控制图相比,联产品多元残差T²控制图能降低控制图虚发警报的概率。针对多元残差T²控制图发现的异常模式,采用支持向量机模型对异常模式进行分类处理,寻找分类规则,构造PSO-SVM分类器,运用粒子群算法对SVM参数寻优,并对得出的结果进行对比分析。分析表明该分类器能提高分类正确率,模式识别可以用于诊断稀土企业引起联产品多元残差T²控制图出现异常的原因,从而提高过程质量管理水平。     

Steady-state-optimal adaptive control charts based on variable sampling intervals

Variable sampling interval (VSI) control charts vary the sampling rate adaptively as a function of the data coming from the process in order to reduce the detection delay of process changes. Zero-time performance refers to the detection delay of a process change that is present during the onset of the chart at time zero. Steady-state performance refers to the detection delay of a process change that occurs after the chart has been operating for some time. The zero-time performance of a VSI control chart can differ considerably from the chart's steady-state performance, which is generally more important than the zero-time performance. We develop an efficient quadratic-programming algorithm for the construction and investigation of steady-state-optimal sampling policies for various VSI charts. We show that a steady-state-optimal VSI scheme is fundamentally different from the respective zero-time-optimal VSI scheme, and recommend VSI policies based on two sampling intervals for the various types of control charts considered.     

Ageing concepts: An approach based on quantile function

The notion of ageing plays an important role in reliability analysis and in identifying life distributions. Most of the ageing concepts existing in the literature are described on the basis of measures defined in terms of the distribution function. Recently, the role of quantile functions has also been identified as lifetime models, and reliability functions based on distribution functions were redefined in terms of quantile functions. In the present paper, we redefine some important popular ageing concepts using quantile functions. The uses of new definitions are illustrated by discussing ageing properties of some quantile function models.     

稳健残差控制图的构建及在金融时序中的应用

对于呈现自相关和波动族聚性并存的受控过程,通常采用残差控制图对其进行监控。但异常点的存在会对自相关或波动族聚性模型的拟合产生重要影响,使得基于该模型的残差并非独立同分布导致常规残差控制图监控失效。为解决这类问题,本文提出稳健残差控制图。即建立稳健的ARMA模型解决自相关问题从而得到无自相关的残差序列,用稳健的GARCH模型来构建控制图的上下限。模拟和实证研究表明,本文提出的稳健残差控制图具有很好的抗异常点能力并能更好的对金融时间序列的异常现象进行监控。     

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。     

多元统计过程控制中的投影寻踪方法

投影寻踪（projection pursuit,简称PP）方法是通过高维数据在低维投影上的统计分析,由此充分获取高维数据本身的统计和信息的一类统计方法,并在统计过程控制中得到广泛应用,本文主要介绍利用投影寻踪方法在统计过程控制中所构造的新控制图及其优良性质,它们包括：监测线性趋势的M控制图,监测均值微小飘移的PP－CUSUM控制图以及矩链过程的多元控制图。     

基于主成分原理的多元质量控制图的构造

控制图是从事统计质量管理经常使用的重要工具,经过多年的发展和实践,一元质量控制图业已得到普遍推广,可是如何开展多元质量控制仍有许多问题值得探讨。在这篇文章中,我们着重讨论了基于主成分分析的多元质量控制图的构造,并结合从企业现场采集到的数据给出了具体的应用说明。     

Control charts with random interarrival times between successive samplings

In the classical setup used in process monitoring, the times between the collection of successive plotted samples are considered as nonrandom. However, in several real‐life applications, it seems plausible to assume that the time needed to collect the necessary information for plotting a point in the control chart has a stochastic nature. Under this scenario, instead of focusing on the number of points plotted on the chart until an out‐of‐control signal is initiated, the appropriate statistic to look at is the total time until a signal is generated. If we denote by L the run length of a control chart and by Y_t,t = 1,2,…, the times between successive plotted points, then the compound random variable expresses the time to signal of a monitoring scheme, under a particular sampling policy. In this paper, we illustrate how S_L can be exploited to study various charts that are suitable for monitoring Poisson observations. We provide some results for the exact distribution of S_L that may facilitate the task of the performance assessment of a control chart with random plotting times; illustrations and several numerical comparisons that are useful for quality control experts who wish to practice them are presented, and finally, an illustrative example elucidating the implementation of the proposed model is also provided.     

Control charts based on parameter depths

Control charts are designed to monitor on-going production processes by tracking subsequent samples of the production using some statistic of a quality characteristic. We propose to track the parameter depths of estimates of a parameter by means of depth (D)-charts, or the associated depth-based ranks by means of r-charts. More precisely, given a general parameter (e.g. mean, standard deviation or pair given by mean and standard deviation) and some historical data of the production, the parameter depth of an estimate of the parameter on new samples of the production with regard to the historical data is computed. The process is considered to be out-of-control when the depth of the estimate of the parameter falls below some given threshold (control limit). Some control limits of specific D-charts are obtained under the assumption of normality of the quality characteristic.     

CUSUM control charts based on likelihood ratio for preliminary analysis

To detect and estimate a shift in either the mean and the deviation or both for the preliminary analysis, the statistical process control (SPC) tool, the control chart based on the likelihood ratio test (LRT), is the most popular method. Sullivan and woodall pointed out the test statistic lrt(n1, n2) is approximately distributed as x2(2) as the sample size n,n1 and n2 are very large, and the value of n1 = 2,3,..., n - 2 and that of n2 = n - n1. So it is inevitable that n1 or n2 is not large. In this paper the limit distribution of lrt(n1, n2) for fixed n1 or n2 is figured out, and the exactly analytic formulae for evaluating the expectation and the variance of the limit distribution are also obtained. In addition, the properties of the standardized likelihood ratio statistic slr(n1, n) are discussed in this paper. Although slr(n1, n) contains the most important information, slr(i, n)(i≠n1) also contains lots of information. The cumulative sum (CUSUM) control chart can obtain more information in this condition. So we propose two CUSUM control charts based on the likelihood ratio statistics for the preliminary analysis on the individual observations. One focuses on detecting the shifts in location in the historical data and the other is more general in detecting a shift in either the location and the scale or both. Moreover, the simulated results show that the proposed two control charts are, respectively, superior to their competitors not only in the detection of the sustained shifts but also in the detection of some other out-of-control situations considered in this paper.     

Consistency of kernel‐based quantile regression

Quantile regression is used in many areas of applied research and business. Examples are actuarial, financial or biometrical applications. We show that a non‐parametric generalization of quantile regression based on kernels shares with support vector machines the property of consistency to the Bayes risk. We further use this consistency to prove that the non‐parametric generalization approximates the conditional quantile function which gives the mathematical justification for kernel‐based quantile regression. Copyright © 2008 John Wiley & Sons, Ltd.     

Model selection method based on maximal information coefficient of residuals

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this article, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coefficient (MIC), a recently proposed dependence measure, captures a wide range of associations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient (PMIC) is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

分数填补下两总体分位数差异的半经验似然推断

在完全随机缺失机制情形,利用分数填补法填补缺失值,然后用经验似然方法构造两总体分位数差异的半经验似然比统计量,证明其渐近服从加权X~2分布并构造了相应的半经验似然置信区间.     

Simple outlier labeling based on quantile regression,with application to the steelmaking process

This paper introduces some methods for outlier identification in the regression setting, motivated by the analysis of steelmaking process data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening with univariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is based on quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditional quantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity, allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experiments have been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by some real life examples, taking as the response variable the energy consumed in the melting process. Copyright © 2015 John Wiley & Sons, Ltd.     

一种多指标质量动态控制图及其应用

研究了将多指标在一个图上标示的多指标控制图。提出了三种质量控制限并作了比较。控制上限(UCL)取Hotelling的T2统计量的α/2上侧分位数。预控上限(UPCL)取Hotelling的T2统计量的λα/2上侧分位数。当第k个观测点的T2k值超过UPCL时,则接下去的抽样区间将缩短,以便及早发现失控状态。对超出控制限的情况下如何确定哪一个质量指标发生异常的问题,提出了一种可靠的分析方法。用这种方法解决了某卷烟厂的烟丝生产过程中的多指标质量控制和管理问题。     

Improving the Performance of the Chi-square Control Chart via Runs Rules

The most popular multivariate process monitoring and control procedure used in the industry is the chi-square control chart. As with most Shewhart-type control charts, the major disadvantage of the chi-square control chart, is that it only uses the information contained in the most recently inspected sample; as a consequence, it is not very efficient in detecting gradual or small shifts in the process mean vector. During the last decades, the performance improvement of the chi-square control chart has attracted continuous research interest. In this paper we introduce a simple modification of the chi-square control chart which makes use of the notion of runs to improve the sensitivity of the chart in the case of small and moderate process mean vector shifts.      

Hotelling’s T charts with variable sample size and control limit

The ideas of variable sampling interval (VSI), variable sample size (VSS), variable sample size and sampling interval (VSSI), and variable parameters (VP) in the univariate case have been successfully applied to the multivariate case to improve the efficiency of Hotelling’s T² chart with fixed sampling rate (FSR) in detecting small process shifts. However, the main disadvantage in using most of these control schemes is an increasing in the complexity due to the adaptive changes in sampling intervals. In this paper, retaining the lengths of sampling intervals constant, a variable sample size and control limit (VSSC) T² chart is proposed and described. The statistical efficiency of the VSSC T² chart in terms of the average time to signal a shift in process mean vector is compared with that of the VP, VSSI, VSS, VSI, and FSR T² charts. From the results of comparison, it shows that the VSSC T² chart for a (very) small shift in the process mean vector gives a better performance than the VSSI, VSS, VSI, and FSR T² charts; meanwhile, it presents a similar performance to the VP T² chart. Furthermore, from the viewpoint of practicability, it is more convenient for administrating the control chart than the VSI, VSSI, and VP T² chart. Thus, it may provide a good option for quick response to small shifts in a multivariate process.     

可变抽样区间的非参数控制图

最近几年一些学者研究了可变抽样区间的质量控制图。Amin等提出了可变抽样区间(VSI)的非参数控制图———符号 (Sign)统计量图〔1〕。本文在此基础上研究位置VSI符号控制图的制定方法 ,并设计离散VSI符号控制图。符号控制图的优点是对非正态总体亦可应用 ,并且不需要过程方差的信息。本文将所设计的VSI符号控制图同固定抽样区间 (FSI)的常规图作比较 ,并举实例说明符号控制图的应用     

Control charts for monitoring correlated counts with a finite range

Correlated count data processes with a finite range can be adequately described by a first‐order binomial autoregressive model. However, in several practical applications, these data demonstrate extra‐binomial variation, and a more appropriate choice is the first‐order beta‐binomial autoregressive model. In this paper, we propose and study control charts that can be used for the monitoring of these 2 processes. Practical guidelines concerning their statistical design are provided, whereas the effect of the extra‐binomial variation is investigated as well. Finally, the practical application of the proposed schemes is illustrated via a real‐data example.     

A note on bootstrapping the variance of sample quantile

Summary Letm _{n, p} denote thep-th quantile based onn observations and let λ _p denote the population quantile. In this paper consistency of the bootstrap estimate of variance of is established.