监测Lévy稳定过程均值变点的平均运行时间估计

平均运行长度(时间)(ARL)是判断一控制图监测变点效果好坏的一个重要工具.本文主要研究Lévy稳定过程的均值变点监测问题.我们给出了三个控制图,即EWMA,GEWMA和GLR的ARL估计,并通过数值模拟比较了4个控制图监测均值变点的效果和差异.     

监测L\'{e}vy稳定过程均值变点的平均运行时间估计

平均运行长度(时间) (ARL)是判断一控制图监测变点效果好坏的一个重要工具\bd 本文主要研究L\'{e}vy 稳定过程的均值变点监测问题\bd 我们给出了三个控制图, 即EWMA, GEWMA和GLR的ARL 估计, 并通过数值模拟比较了4个控制图监测均值变点的效果和差异.     

基于改进加权Poisson CUSUM控制图的发病率监控研究

现代社会医疗质量管理的重要性不言而喻,如何科学有效地开展发病率监控,具有重要的研究意义和实用价值。本文主要研究加权Poisson CUSUM控制图的改进及其在发病率监控上的应用。通过引入"标准化"处理思想,即将对数似然比统计量除以对应样本容量,本文提出"标准化"对数似然比加权Poisson CUSUM控制图,用以提高对发病率的监控水平。通过设定合适的人口模型,模拟计算验证了本文设计的模型能够有效提高监控效果,且对于不同人口模型和不同加权函数均有显著优势。最后,基于美国新墨西哥州男性甲状腺癌患病数据开展的实证检验也印证了以上结论。     

带有测量误差的相关数据的CUSUM控制图

实际中测量误差不仅存在而且在某些情况下还影响质量控制的表现,本文将考虑带有测量误差的相关数据的监控问题. 为检测这类数据的飘移,我们给出了一个基于极大似然比检验的CUSUM控制图及其多种情况下的可控与失控的ARL.模拟结果显示, 当过程负相关时, 我们提出的CUSUM控制图具有良好的表现.     

CUSUM，GLR，GEWMA以及RFCuscore控制图监测稳定过程均值漂移的效果比较

本文给出了累积和控制图(CUSUM)监测稳定过程均值漂移的平均运行长度(ARL)的区间估计,并采用数字模拟的方法对CUSUM,GLR,GEWMA以及RFCuscore四种控制图监测稳定过程均值漂移的效果进行比较,结果显示CUSUM效果最好.     

Parameter change test for location‐scale time series models with heteroscedasticity based on bootstrap

This study considers the bootstrap cumulative sum (CUSUM) test for a parameter change in location‐scale time series models with heteroscedasticity. The CUSUM test has been popular for detecting an abrupt change in time series models because it performs well in many applications. However, it has severe size distortions in many situations. As a remedy, we consider the bootstrap CUSUM test, particularly focusing on the CUSUM test based on score vectors, and demonstrate the weak consistency of the bootstrap test for its justification. A simulation study and data analysis are conducted for illustration.     

Nonparametric Multivariate CUSUM Chart and EWMA Chart Based on Simplicial Data Depth

Simplicial data depth is a useful tool for describing how central a vector is in a multivariate distribution. If the average simplicial depth of a subgroup of observations from a multivariate distribution is too small, it may indicate that a shift in its location or/both scale occurs. In this paper, we propose two new types of nonparametric control charts which are one-sided CUSUM and EWMA control schemes based on simplicial data depth. We also compute the Average Run Length of the CUSUM chart and the EWMA chart by Markov chain method. Recommendations on how to choose the optimal reference value and the smoothing parameter are also given. Comparisons between these two proposed control schemes and the multivariate EWMA are presented.     

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。     

Monitoring the Zero-Inflated Time Series Model of Counts with Random Coefficient

In this research, we consider monitoring mean and correlation changes from zero-inflated autocorrelated count data based on the integer-valued time series model with random survival rate. A cumulative sum control chart is constructed due to its efficiency, the corresponding calculation methods of average run length and the standard deviation of the run length are given. Practical guidelines concerning the chart design are investigated. Extensive computations based on designs of experiments are conducted to illustrate the validity of the proposed method. Comparisons with the conventional control charting procedure are also provided. The analysis of the monthly number of drug crimes in the city of Pittsburgh is displayed to illustrate our current method of process monitoring.     

几类正态过程的CUSUM图

本文针对呈正态分布的过程数据, 讨论当过程参数$(\mu,\sigma^2)$已知或未知时单参数变化、双参数变化的过程控制问题. 针对均值未知情况,利用Quesenberry提出的$Q$统计量构造了一系列标准化的CUSUM图;对于方差未知时的复杂情形, 给出了一种构造更为简单的CUSUM图的方法;对于未知(或不关心) $(\mu,\sigma^2)$中何者变化的特殊情形, 文章提出了纯变化的概念, 给出了相应统计量及由该统计量构造的CUSUM-D图, 针对每个考虑的情形, 通过模拟计算本文给出了相应于统计量的条件期望延时(CED), 同时在文章最后给出了本文提出的CUSUM图与已有的CUSUM图($Q$图)的模拟比较, 结果表明新的CUSUM图是可行的.