Quality Control Chart for the Mean using Double Ranked Set Sampling

In this paper, an attempt is made to develop Quality Control Charts for monitoring the process mean based on Double Ranked Set Sampling (DRSS) rather than the traditional Simple Random Sampling (SRS). Considering a normal population and several shift values, the performance of the Average Run Length (ARL) of these new charts was compared with the control charts based on Ranked Set Sampling (RSS) and SRS with the same number of observations. It is shown that the new charts do a better job of detecting changes in process mean compared with SRS and RSS.     

横向扩展，纵向连锁，动静结合，创建思维新机制——广州人思维机制建构探索

本篇是系列论文第二篇，紧扣广州人思维机制建构问题展开。从广州人创建思维新机制看，思维是以横向扩展（“静”中有“动”）作基础，并以纵向推移（“静”“动”结合）作深化，最终实现纵横交错的动态系统，从而全面具体把握现实发展     

MOSFET中1/f噪声的Hooge参数α_H

本文报道 P 沟道 MOSFET 器件中 1/f 噪声 Hooge 参数测量的部分实验结果.由加利福尼亚州的 SILICONIX 公司提供的实验样品是采用同样工艺作在同一块基片之上的.实验表明,在由非谐和状态到谐和状态过渡时,α_H 与沟道长度的平方成正比.类似的结果未曾见报道.     

收敛函数列的曲线收敛与弧长收敛

本文系统讨论了收敛函数列的曲线收敛与弧长收敛,并给出和证明了弧长收敛的一个性质定理和一个判定定理。     

QUALITY CONTROL WITH MULTIVARIATE DATA

The paper examines statistical process control of bivariate and multivariate data, using in particular the multivariate equivalents of the univariate Shewhart chart, CUSUM chart and the Exponentially Weighted Moving Average chart. This illustrates the usefulness of Principal Component methods in statistical process control with multivariate data.     

媒介研究中的科技与文化——对媒介决定论的再思考

在媒介文化研究中存在着两种截然不同的观点：媒介决定论和社会文化选择论。媒介决定论认为新科技带来新的社会文化形式;而社会文化选择论则认为先有社会文化语境的潜在“召唤”,然后才出现相应的科技形式。随着新媒介的出现,媒介决定论似乎占据主流,但是文化选择论对媒介决定论又起到很好的补充作用,它们之间是一种互补关系。新媒介的出现应该是在科技的进步和文化“召唤”的合力作用下产生的。     

Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression

The monitoring of process/product profiles is presently a growing and promising area of research in statistical process control. This study is aimed at developing monitoring schemes for nonlinear profiles with random effects. We utilize the technique of principal components analysis to analyze the covariance structure of the profiles and propose monitoring schemes based on principal component (PC) scores. The number of the PC scores used in constructing control charts is crucial to the detecting power. In the Phase I analysis of historical data, due to the dependency of the PC-scores, we adopt the usual Hotelling T ² chart to check the stability. For Phase II monitoring, we study individual PC-score control charts, a combined chart scheme that combines all the PC-score charts, and a T ² chart. Although an individual PC-score chart may be perfect for monitoring a particular mode of variation, a chart that can detect general shifts, such as the T ² chart and the combined chart scheme, is more feasible in practice. The performances of the schemes under study are evaluated in terms of the average run length.     

A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart

A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control chart is proposed. The new multivariate scheme can detect small and large shifts in the process mean vector effectively. The proposed scheme can be viewed as a smooth combination of a multivariate exponentially weighted moving average (MEWMA) chart and a Shewhart χ²-chart. The optimal design of the proposed chart is given according to a pre-specified in-control average run length and two shift sizes; a small and large shift each measured in terms of the non centrality parameter. The signal resistance of the newly proposed multivariate chart is also given. Comparisons among the new chart, the MEWMA chart, and the combined Shewhart-MEWMA (S-MEWMA) chart in terms of the standard and worst-case average run length profiles are presented. In addition, the three charts are compared with respect to their worst-case signal resistance values. The proposed chart gives somewhat better worst-case ARL and signal resistance values than the competing charts. It also gives better standard ARL performance especially for moderate and large shifts. The effectiveness of our proposed chart is illustrated through an example with simulated data set.     

Comparing Equal-Tail Probability and Unbiased Confidence Intervals for the Intraclass Correlation Coefficient

The conventional confidence interval for the intraclass correlation coefficient assumes equal-tail probabilities. In general, the equal-tail probability interval is biased and other interval procedures should be considered. Unbiased confidence intervals for the intraclass correlation coefficient are readily available. The equal-tail probability and unbiased intervals have exact coverage as they are constructed using the pivotal quantity method. In this article, confidence intervals for the intraclass correlation coefficient are built using balanced and unbalanced one-way random effects models. The expected length of confidence intervals serves as a tool to compare the two procedures. The unbiased confidence interval outperforms the equal-tail probability interval if the intraclass correlation coefficient is small and the equal-tail probability interval outperforms the unbiased interval if the intraclass correlation coefficient is large.     

A Distribution-Free Control Chart Based on Order Statistics

In this article, we introduce a new distribution-free Shewhart-type control chart that takes into account the location of a single order statistic of the test sample (such as the median) as well as the number of observations in that test sample that lie between the control limits. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of the chart is that, due to its nonparametric nature, the false alarm rate and in-control run length distribution are the same for all continuous process distributions, and so will be naturally robust. Tables are provided for the implementation of the chart for some typical ARL values and false alarm rates. The empirical study carried out reveals that the new chart is preferable from a robustness point of view in comparison to a classical Shewhart-type chart and also the nonparametric chart of Chakraborti et al. (2004 Chakraborti , S. , van der Laan , P. , van de Wiel , M. A. ( 2004 ). A class of distribution-free control charts . J. Roy. Statist. Soc. Ser. C-Appl. Statist. 53 ( 3 ): 443 – 462 .[Web of Science ®] , [Google Scholar]).