Multivariate process dispersion monitoring without subgrouping

The memory-type adaptive and non-adaptive control charts are among the best control charts for detecting small-to-moderate changes in the process parameter(s). In this paper, we propose the Crosier CUSUM (CCUSUM), EWMA, adaptive CCUSUM (ACCUSUM) and adaptive EWMA (AEWMA) charts for efficiently monitoring the changes in the covariance matrix of a multivariate normal process without subgrouping. Using extensive Monte Carlo simulations, the length characteristics of these control charts are computed. It turns out that the ACCUSUM and AEWMA charts perform uniformly and substantially better than the CCUSUM and EWMA charts when detecting a range of shift sizes in the covariance matrix. Moreover, the AEWMA chart outperforms the ACCUSUM chart. A real dataset is used to explain the implementation of the proposed control charts.     

Generally weighted moving average control charts with fast initial response features

The generally weighted moving average (GWMA) control chart is an extension model of exponentially weighted moving average (EWMA) control chart. Recently, some approaches have been proposed to modify EWMA charts with fast initial response (FIR) features. We introduce these approaches in GWMA-type charts. Via simulation, various control schemes are designed and then their average run lengths are computed and compared. Based on the overall performance, it is showed that the DGWMA chart is the best choice especially when the shift is moderate, and the GWMA charts provided with additional FIR feature have a good performance only in detecting large shifts during the initial stage.     

Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.     

New V control chart for the Maxwell distribution

Control charts have been popularly used as a user-friendly yet technically sophisticated tool to monitor whether a process is in statistical control or not. These charts are basically constructed under the normality assumption. But in many practical situations in real life this normality assumption may be violated. One such non-normal situation is to monitor the process variability from a skewed parent distribution where we propose the use of a Maxwell control chart. We introduce a pivotal quantity for the scale parameter of the Maxwell distribution which follows a gamma distribution. Probability limits and L-sigma limits are studied along with performance measure based on average run length and power curve. To avoid the complexity of future calculations for practitioners, factors for constructing control chart for monitoring the Maxwell parameter are given for different sample sizes and for different false alarm rate. We also provide simulated data to illustrate the Maxwell control chart. Finally, a real life example has been given to show the importance of such a control chart.     

A direct procedure for monitoring the coefficient of variation using a variable sample size scheme

A variable sample size (VSS) scheme directly monitoring the coefficient of variation (CV), instead of monitoring the transformed statistics, is proposed. Optimal chart parameters are computed based on two criteria: (i) minimizing the out-of-control ARL (ARL₁) and (ii) minimizing the out-of-control ASS (ASS₁). Then the performances are compared between these two criteria. The advantages of the proposed chart over the VSS chart based on the transformed statistics in the existing literature are: the former (i) provides an easier alternative as no transformation is involved and (ii) requires less number of observations to detect a shift when ASS₁ is minimized.     

Exponentially weighted moving average charts for correlated multivariate Poisson processes

In this article, we study exponentially weighted moving average (EWMA) control schemes to monitor the multivariate Poisson distribution with a general covariance structure, so that the practitioner can simultaneously monitor multiple correlated attribute processes more effectively. The statistical performance of the charts is assessed in terms of the run length properties and compared against other mainstream attribute control schemes. The application of the proposed methods to real-life and simulated datasets is demonstrated.     

武汉市民营科技企业投资环境分析

在知识逐渐取代劳动力和自然资源而成为主要生产要素的前提下,发展民营科技企业是振兴地区经济发展的重要支撑点。武汉市民营科技企业具有地区分布不均衡,产业趋向集中,创新能力强等特征。借助武汉的区位及科技资源等优势,武汉市民营科技企业应发挥科技特色,加强制度创新,加大投资力度,拓宽融资渠道,创新投资环境,为其发展创造良好的外部条件。     

依法治国的宪法性思考--兼论江泽民同志十六大报告的法治精神

崇尚法治是现代社会政治文明的重要标志。十六大报告提出的依法治国思想的核心和灵魂是依宪治国。法治、宪政与政治文明三者有内在的密不可分的关系。从宪法角度讲,有中国特色的法治制度构建包括建立与国际接轨的法律规范体系、积极实现无限政府向有限政府的过渡、建立科学合理的宪法性审查制度、确立全面司法审查原则的宪法地位等方面。     

The extended GWMA control chart

This study extends the generally weighted moving average (GWMA) control chart by imitating the double exponentially weighted moving average (DEWMA) technique. The proposed chart is called the double generally weighted moving average (DGWMA) control chart. Simulation is employed to evaluate the average run length characteristics of the GWMA, DEWMA and DGWMA control charts. An extensive comparison of these control charts reveals that the DGWMA control chart with time-varying control limits is more sensitive than the GWMA and the DEWMA control charts for detecting medium shifts in the mean of a process when the shifts are between 0.5 and 1.5 standard deviations. Additionally, the GWMA control chart performs better when the mean shifts are below the 0.5 standard deviation, and the DEWMA control performs better when the mean shifts are above the 1.5 standard deviation. The design of the DGWMA control chart is also discussed.     

CUSUM Control Charts for Multivariate Poisson Distribution

A cumulative sum control chart for multivariate Poisson distribution (MP-CUSUM) is proposed. The MP-CUSUM chart is constructed based on log-likelihood ratios with in-control parameters, Θ₀, and shifts to be detected quickly, Θ₁. The average run length (ARL) values are obtained using a Markov Chain-based method. Numerical experiments show that the MP-CUSUM chart is effective in detecting parameter shifts in terms of ARL. The MP-CUSUM chart with smaller Θ₁ is more sensitive than that with greater Θ₁ to smaller shifts, but more insensitive to greater shifts. A comparison shows that the proposed MP-CUSUM chart outperforms an existing MP chart.