Effect of measurement error on exponentially weighted moving average control charts under ranked set sampling schemes

Control charts are a powerful statistical process monitoring tool often used to monitor the stability of manufacturing processes. In quality control applications, measurement errors adversely affect the performance of control charts. In this paper, we study the effect of measurement error on the detection abilities of the exponentially weighted moving average (EWMA) control charts for monitoring process mean based on ranked set sampling (RSS), median RSS (MRSS), imperfect RSS (IRSS) and imperfect MRSS (IMRSS) schemes. We also study the effect of multiple measurements and non-constant error variance on the performances of the EWMA control charts. The EWMA control chart based on simple random sampling is compared with the EWMA control charts based on RSS, MRSS, IRSS and IMRSS schemes. The performances of the EWMA control charts are evaluated in terms of out-of-control average run length and standard deviation of run lengths. It turns out that the EWMA control charts based on MRSS and IMRSS schemes are better than their counterparts for all measurement error cases considered here.     

Evaluation of the economic statistical design of the multivariate T2 control chart with multiple variable sampling intervals scheme: NSGA-II approach

The economic and statistical merits of a multiple variable sampling intervals scheme are studied. The problem is formulated as a double-objective optimization problem with the adjusted average time to signal as the statistical objective and the expected cost per hour as the economic objective. Bai and Lee's [An economic design of variable sampling interval ¯X control charts. Int J Prod Econ. 1998;54:57–64] economic model is considered. Then we find the Pareto-optimal designs in which the two objectives are minimized simultaneously by using the non-dominated sorting genetic algorithm. Through an illustrative example, the advantages of the proposed approach are shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach.     

A Probability theory of hierarchic clustering using random dendrograms

It is widely recognized that a major, current problem in cluster analysis is that of validating results. This paper looks at one possible approach to the validation of results. It considers the structure of (unlabelled) dendrograms and proposes a model of random dendrograms. Algorithms for calculating the probability distribution of a coefficient of structure of a dendrogram are discussed, —both by enumeration of distinct dendrograms, and by Monte Carlo generation of dendrograms     

Using Predictive Risk for Process Control

When monitoring highly capable processes, it is often desirable to tolerate small instabilities in order to avoid tempering. One approach in this setting is to monitor the capability of the process dynamically and signal if the estimated capability reaches an unacceptably low level. We suggest that monitoring the probability of the next item not falling between the specification limits is a more natural scale to evaluate risk, and offers appreciable flexibility. We use a statistical model and a window of data to evaluate this probability and decide if the process should be halted immediately based on that estimate. The properties of this method are explored numerically and a case study is provided.     

A new distribution-free control scheme based on order statistics

We establish a class of nonparametric Shewhart-type control charts based on a reference sample drawn from the process. The proposed nonparametric control chart takes advantage of the location of two different order statistics of the reference and test sample respectively. The decision rule of the new monitoring scheme is filled out by the number of test observations that are located between the control limits. The general setup of the new class of control charts is presented in detail, while the operating characteristic function is studied for both in- and out-of-control processes. Closed formulae for the evaluation of the alarm rate and the average run length are concluded for plausible shift in the underlying distribution to Lehmann alternatives. Several numerical results, displayed for the new family of nonparametric control charts, depict that the proposed control scheme attains competitive performance.     

How can we make better graphs? An initiative to increase the graphical expertise and productivity of quantitative scientists

Graphics are at the core of exploring and understanding data, communicating results and conclusions, and supporting decision‐making. Increasing our graphical expertise can significantly strengthen our impact as professional statisticians and quantitative scientists. In this article, we present a concerted effort to improve the way we create graphics at Novartis. We provide our vision and guiding principles, before describing seven work packages in more detail. The actions, principles, and experiences laid out in this paper are applicable generally, also beyond drug development, which is our field of work. The purpose of this article is to share our experiences and help foster the use of good graphs in pharmaceutical statistics and beyond. A Graphics Principles “Cheat Sheet” is available online at https://graphicsprinciples.github.io/ .     

Constrained nonparametric maximum-likelihood estimation for mixture models

A nonparametric mixture model specifies that observations arise from a mixture distribution, ∫ f(x, θ) dG(θ), where the mixing distribution G is completely unspecified. A number of algorithms have been developed to obtain unconstrained maximum-likelihood estimates of G, but none of these algorithms lead to estimates when functional constraints are present. In many cases, there is a natural interest in functional ?(G), such as the mean and variance, of the mixing distribution, and profile likelihoods and confidence intervals for ?(G) are desired. In this paper we develop a penalized generalization of the ISDM algorithm of Kalbfleisch and Lesperance (1992) that can be used to solve the problem of constrained estimation. We also discuss its use in various different applications. Convergence results and numerical examples are given for the generalized ISDM algorithm, and asymptotic results are developed for the likelihood-ratio test statistics in the multinomial case.     

Local influence for incomplete data models

This paper proposes a method to assess the local influence in a minor perturbation of a statistical model with incomplete data. The idea is to utilize Cook's approach to the conditional expectation of the complete-data log-likelihood function in the EM algorithm. It is shown that the method proposed produces analytic results that are very similar to those obtained from a classical local influence approach based on the observed data likelihood function and has the potential to assess a variety of complicated models that cannot be handled by existing methods. An application to the generalized linear mixed model is investigated. Some illustrative artificial and real examples are presented.     

突发社会危机统计监测指标体系的设计

当前,我国正处于全面深化改革的深水区,各种利益主体空前分化、社会矛盾错综复杂,导致突发社会危机频发。突发社会危机的常态管理已逐渐成为社会安全治理的主导方式。对突发社会危机"苗头性"、"倾向性"信号进行监测性搜集,既应当从被观测者的各种客观"境遇状态"上做数量测度,又必须从被观测者的主观体验上做感知调查,使用客观测量指标和主观感受指标相结合的方式,遵循针对性、全面性、系统性、可行性等基本原则进行相关的指标体系设计。基于观察突发社会危机诱因变动与系统记录突发社会危机各种可能征兆的认识,在厘清统计监测与统计预警评估关系的同时,着重从经济利益、政治权益、社会保障、文化教育、公共安全、民族宗教等六个方面,根据原因导向设计构建了一套指向一般公众、家庭和社区的突发社会危机统计监测指标体系。     

车牌额度拍卖市场管制有效性及效率改进空间

随着我国城镇化和机动化进程加速,已有多个城市加入私车拥有权管制行列,管制政策有效性亟待科学分析和系统评估。基于管制绩效评估理论,以上海车牌额度拍卖市场为切入点,首先,通过限价之前拍卖市场关键变量近线性关系发现其具有量价齐升、正向激励特征,Hurst 指数分析表明拍卖价格变化率可恢复均值、回归中位。其次,选择三种对照组,采用差中差、OLS 等方法计量减缓私人汽车拥有量增速的宏观效应以及通过额度投放量稳定拍卖市场的微观效应,发现管制效果显著。进一步,根据高斯回归过程考察拍卖价格可预测性,指明提升效率、放松管制的改进方向和措施,为政策完善提供有价值参考。