自适应阈值估计的MS模型仿真建模

提出了一种改进的模拟电路多信号模型(MS模型)的仿真建模方法。采用Monte-Carlo方法实现电路的仿真,以获取电路特征的统计数据,利用经验公式法确定Monte-Carlo仿真的分析次数,可以减少仿真时间开销;同时,根据统计数据检验该特征的统计分布情况,再以此为依据采用自适应方法确定该特征的阈值估计区间,从而提高模型精度。最后进行了实例分析,验证了该建模方法的有效性;并从仿真时间与建模精度两个方面进行了性能比对,说明其具有的优势。     

一篇存在严重文献与逻辑失误的考订文章——吴伟斌先生《关于元稹知制诰以及翰林承旨学士任内的几个问题》商榷

吴伟斌先生《关于元稹知制诰以及翰林承旨学士任内的几个问题》一文,讨论了元稹在元和、长庆之际仕途升迁的几个问题,如元稹自虢州回长安的时间、元稹解翰林学士任的时间等,但令人遗憾的是,他的考订存在较多、较严重的失误。清理这些失误,对元稹研究将具有重要的意义。     

Evaluation of Measurement Comparisons Using Generalized Least Squares: the Role of Participants’ Estimates of Systematic Error

We consider the evaluation of laboratory practice through the comparison of measurements made by participating metrology laboratories when the measurement procedures are considered to have both fixed effects (the residual error due to unrecognised sources of error) and random effects (drawn from a distribution of known variance after correction for all known systematic errors). We show that, when estimating the participant fixed effects, the random effects described can be ignored. We also derive the adjustment to the variance estimates of the participant fixed effects due to these random effects.     

On best affine prediction

The theory of best affine prediction (BAP) is extended to the vector case with possibly singular variance matrix of the predictor variable. The theory is then applied to derive Thomson’s classical predictor for factor scores, allowing for a singular variance matrix of the factors. The results are formulated in a free distribution setting. Further, Bartlett’s estimator is considered and compared with Thomson’s predictor. The authors are thankful to the two referees, one for a suggestion that led to the Addendum of the paper, and the other one for several very useful remarks. Research supported by the Spanish grant BEC2000-0983.     

Multivariate Quality Control Chart for Autocorrelated Processes

Traditional multivariate statistical process control (SPC) techniques are based on the assumption that the successive observation vectors are independent. In recent years, due to automation of measurement and data collection systems, a process can be sampled at higher rates, which ultimately leads to autocorrelation. Consequently, when the autocorrelation is present in the data, it can have a serious impact on the performance of classical control charts. This paper considers the problem of monitoring the mean vector of a process in which observations can be modelled as a first-order vector autoregressive VAR (1) process. We propose a control chart called Z-chart which is based on the single step finite intersection test (Timm, 1996). An important feature of the proposed method is that it not only detects an out of control status but also helps in identifying variable(s) responsible for the out of control situation. The proposed method is illustrated with the help of suitable illustrations.     

Further exploratory analysis of split-plot experiments to study certain stratified effects

Designed experiments are a key component in many companies' improvement strategies. Because completely randomized experiments are not always reasonable from a cost or physical perspective, split-plot experiments are prevalent. The recommended analysis accounts for the different sources of variation affecting whole-plot and split-plot error. However experiments on industrial processes must be run and, consequently analyzed quite differently from ones run in a controlled environment. Such experiments are typically subject to a wide array of uncontrolled, and barely understood, variation. In particular, it is important to examine the experimental results for additional, unanticipated sources of variation. In this paper, we consider how unanticipated, stratified effects may influence a split-plot experiment and discuss further exploratory analysis to indicate the presence of stratified effects. Examples of such experiments are provided, additional tests are suggested and discussed in light of their power, and recommendations given.     

A practical comparison of blinded methods for sample size reviews in survival data clinical trials

This paper presents practical approaches to the problem of sample size re-estimation in the case of clinical trials with survival data when proportional hazards can be assumed. When data are readily available at the time of the review, on a full range of survival experiences across the recruited patients, it is shown that, as expected, performing a blinded re-estimation procedure is straightforward and can help to maintain the trial's pre-specified error rates. Two alternative methods for dealing with the situation where limited survival experiences are available at the time of the sample size review are then presented and compared. In this instance, extrapolation is required in order to undertake the sample size re-estimation. Worked examples, together with results from a simulation study are described. It is concluded that, as in the standard case, use of either extrapolation approach successfully protects the trial error rates.     

中国文化产业区域差异的空间计量分析

基于2004年与2008年文化产业国家标准统计数据,分别利用Theil指数与Moran’s I指数对中国文化产业的区域差异与空间自相关性进行了衡量,并运用空间计量经济模型对要素禀赋、集聚经济与产业政策因素对文化产业区域差异的影响进行了实证检验。研究发现：中国文化产业区域差异明显,呈现东高西低的梯度分布格局,但区域间文化产业发展呈收敛趋势;中国文化产业区域间存在不断增强的正向空间相关性与空间集聚性;要素禀赋、集聚经济与产业政策均能解释文化产业的区域差异;文化产业的空间溢出效应有利于缩小区域间的文化产业差异。     

Robustness of the two-sample t-test umdeh violations of the homogeneity of farjance assumptioi

When the two-sample t-test has equal sample slies, it is widely considered to be a robust procedure (with respect to the significaoce level) under violatioa of the assuaptioo of equal variances. This paper is coa-earned with a quantification of the amount of robustness which this procedure has under such violations, The approach is through the concept of "religion of robustness" and the resluts show an extremely strong degree of robustness for the equal an extremely strong degree of robustness for the equal sample size t-test, probably more so than most statistyicians realise. This extremely high level of robustness, however, reduces quickly as the sample sizes begin to vary from equality. The regions of robustnes obtained show that while most users would likely be satisfied with the degree of robustness inherent when the two sample sizes each vary by 10% from equality, most would wish to be much more cautions when the variation is 20%. The study covers sample sizes n₁ -= n ² = 5(5)30(10)50 plus 10% and 20% variations thereof for the two-tailed test and nominal significance levels of 0.01 and 0.05.