关于空间$S(\Omega,\Sigma,\mu)$与$L^\beta(\Omega,\Sigma,\mu)$上的次加上半连续$\alpha$-正齐性泛函

本文得到了$S(\Omega,\Sigma,\mu)$和$L^\beta(\Omega,\Sigma,\mu)$分别不存在非零的上半连续、次加、$\alpha$-正齐性泛函(分别有本文得到了$S(\Omega,\Sigma,\mu)$和$L^\beta(\Omega,\Sigma,\mu)$分别不存在非零的上半连续、次加、$\alpha$-正齐性泛函(分别有本文得到了$S(\Omega,\Sigma,\mu)$和$L^\beta(\Omega,\Sigma,\mu)$分别不存在非零的上半连续、次加、$\alpha$-正齐性泛函(分别有本文得到了S（Ω，∑，μ）与L^β（Ω，∑，μ）分别不存在非零的上半连续、次加、α-正齐性泛函（分别有0≤α≤1和β〈α≤1）的充要条件．     

关于正态分布的次序统计量的随机序

设$X_1,X_2,\cdots,X_n$和$X^*_1,X^*_2,\cdots,X^*_n$分别服从正态分布$N(\mu_i,\sigma^2)$和$N(\mu^*_i,\sigma^2)$,以$X_{(1)}$,$X^*_{(1)}$分别表示$X_1,\cdots,X_n$和$X^*_1,\cdots,X^*_n$的极小次序统计量,以$X_{(n)}$, $X^*_{(n)}$分别表示$X_1,\cdots,X_n$和$X^*_1,\cdots$,$X^*_n$的极大次序统计量. 我们得到了如下结果:(i)\,如果存在严格单调函数$f$使得$(f(\mu_{1}),\cdots,f(\mu_{n}))\succeq_{\text{m}}$ $(f(\mu^{*}_{1}),\cdots,f(\mu^{*}_{n}))$,且$f'(x)f'(x)\!\geq\!0$, 则$X_{(1)}\!\leq_{\text{st}}\!X^*_{(1)}$;(ii)\,如果存在严格单调函数$f$使得$(f(\mu_{1})$,$\cdots,f(\mu_{n}))\succeq_{\text{m}}(f(\mu^{*}_{1}),\cdots,f(\mu^{*}_{n}))$,且$f'(x)f'(x)\leq 0$, 则$X_{(n)}\geq_{\text{st}}X^*_{(n)}$.(iii)\,设$X_{1},X_{2},\cdots,X_{n}$和\, $X^*_{1},X^*_{2},\cdots,X^*_{n}$分别服从正态分布$N(\mu,\sigma_i^2)$和$N(\mu,\sigma_i^{*2})$,若$({1}/{\sigma_{1}},\cdots,{1}/{\sigma_{n}})\succeq_{\text{m}}({1}/{\sigma^{*}_{1}},\cdots,{1}/{\sigma^{*}_{n}})$,则有$X_{(1)}\leq_{\text{st}}X^*_{(1)}$和$X_{(n)}\geq_{\text{st}}X^*_{(n)}$同时成立.     

强正则 $(\alpha,\beta)$-族和平移强正则 $(\alpha,\beta)$-几何

本文给出了强正则$(\alpha,\beta)-$族的概念,它是[4]和[5]中$SPG-$族概念的推广.进一步,给出了一种用强正则 $(\alpha,\beta)-$族构造强正则$(\alpha,\beta)-$几何的方法.另外,本文还证明了由强正则$(\alpha,\beta)-$线汇构造的强正则$(\alpha,\beta)-$几何是平移强正则$(\alpha,\beta)-$几何;当$t-r>\beta$时,反之亦成立.     

不完全椭球约束下多元线性模型线性估计的可容许性

本文研究了多元线性模型当未知参数受不完全椭球约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$时线性估计的可容许性问题.具体而言,我们研究了约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$中$N$和非中心点$\Theta_1$对线性估计的可容许性的影响.主要结果表明在两个不同的不完全椭球约束条件$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$与$\mbox{tr}(\Theta-\Theta_2)'N(\Theta-\Theta_2)\leq\sigma^2$ 下,当$\Theta_1$和$\Theta_2$满足一定的关系时,可容许的齐次线性估计类是相同的.     

纵向数据下半参数回归模型估计的渐近性质

本文考虑纵向数据下半参数回归模型: $y_{ij}=x_{ij}'\beta+g(t_{ij})+e_ij},\;i=1,\cdots,m,\;j=1,\cdots,n_i$. 基于最小二乘法和一般的非参数权函数方法给出了模型中参数$\beta$和回归函数$g(\cdot)$的估计, 并在适当条件下证明了$\beta$估计量的渐近正态性和$g(\cdot)$估计量的最优收敛速度\bd 模拟结果表明我们的估计方法在有限样本情形有良好的效果     

亚BCI-代数的$(\alpha,\beta)$-软理想

首先将软集的参数集赋予亚BCI-代数, 给出了亚BCI-代数的$(\alpha,\beta)$-软理想的概念.当$U=[0,1], \alpha=U, \beta=\phi$时,相应地就得到了亚BCI-代数的犹豫模糊理想的概念.研究了亚BCI-代数的$(\alpha,\beta)$-软理想的一些重要性质.最后讨论了亚BCI-代数的$(\alpha,\beta)$-软理想的同态像和原像的性质.     

有结构变化的半参数回归模型

结合半参数回归模型和含未知变点的结构变化模型提出 一个新的模型\,---\,有结构变化的半参数回归模型, 给出了新模型的有关参数$\beta,\beta^\ast,\gamma,k$的加权最小二乘估计和$f(t)$的核估计, 证明了参数\, $\beta,\beta^\ast,\gamma$的估计的$\sqrt{n}$\,-相合性, 强相合性, 讨论了模型的检验等问题, 并进一步通过随机模拟验证了新模型的优越性.     

与强奇异 Calder\'{o}n-Zygmund算子相关的Toeplitz算子的双权估计

研究了与强奇异Calder\'{o}n-Zygmund算子和加权 Lipschitz函数${\rm Lip}_{\beta_0,\omega}$相关的Toeplitz算子$T_b$的sharp极大函数的点态估计,并证明了Toeplitz算子是从 $L^p(\omega)$到$L^q(\omega^{1-q})$上的有界算子.此外, 建立了与强奇异Calder\'{o}n-Zygmund算子和加权 BMO函数${\rm BMO}_{\omega}$相关的Toeplitz算子$T_b$的sharp极大函数的点态估计,并证明了Toeplitz算子是从 $L^p(\mu)$到$L^q(\nu)$上的有界算子.上述结果包含了相应交换子的有界性.     

具有可反测地线的$(\alpha,\beta)$-度量

本文我们得到了$(\alpha,\beta)$-度量的测地系数$G^{i}(x,y)$和其逆$G^{i}(x,-y)$有相同Douglas曲率的充分必要条件.这个充分必要条件恰好是$(\alpha,\beta)$-度量具有可反测地线的充分必要条件.     

具有弱对称应力的线弹性方程的稳定化格式

利用稳定化方法讨论拉格朗日乘子法得到的具有弱对称应力的线弹性问题. 用线性元和分片常数分别逼近变分问题的应力和位移. 并通过添加稳定项$G_1(\cdot,\cdot)$, $G_2(\cdot,\cdot)$和$G_3(\cdot,\cdot)$ 使相应混合离散变分问题满足弱BB条件. 接着详细研究了变分问题的解与稳定混合有限元解之间的误差估计,最后用两个数值算例验证理论分析的有效性.     

控制过程方差的CUSUMQ图及其性质

崔恒建．控制过程方差的ＣＵＳＵＭＱ图及其性质．数理统计与管理，１９９８，１７（４），３３～３８．Ｑｕｓｅｎｂｅｒｒｙ（１９９５）基于样本方差的标准化变换Φ－１［Ｈｎ－１（（ｎ－１）Ｓ２／σ２０）］提出了控制过程方差的累积和（ＣＵＳＵＭ）Ｑ控制图。本文我们描述了在控制过程方差变化中这种ＣＵＳＵＭＱ控制图的性质，并将控制图的设计方法用到单边及双边的ＣＵＳＵＭＱ图，说明它几乎是最优的。而且我们发现在控制过程方差的微小变化时，设计的ＣＵＳＵＭＱ图的性能要优于基于ｌｏｇ（Ｓ２）的ＣＵＳＵＭ和ＥＷＭＡ图     

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In recent years, statistical process control (SPC) has been widely used to monitor the performance of clinical practitioners, such as surgeons and general practitioners. In this paper, two risk-adjusted geometric control charts namely cumulative sum (CUSUM) and weighted likelihood ratio test (WLRT) are proposed to monitor surgery performance in phase II. The performance of the proposed control charts is evaluated and compared by simulation experiments for different shift values in the parameters of a risk-adjusted logistic regression model in terms of the average run length (ARL) criterion. The results show that all methods work well in the sense that they can effectively detect shifts in the process parameters.     

具有可变抽样区间的非正态累积和控制图

当过程存在小波动时,累积和控制图比传统的休哈特控制图监控效果灵敏。为了提高控制图的监控效率,本文针对非正态情形下的累积和控制图进行可变抽样区间设计。首先用Burr分布近似各种非正态分布,构造可变抽样区间的非正态累积和控制图;其次利用马尓可夫链方法计算其平均报警时间;最后研究结果表明, 所设计的可变抽样区间非正态累积和控制图较固定抽样区间的非正态累积和控制图能更好地监控过程的变化。     

Optimization designs and performance comparison of two CUSUM schemes for monitoring process shifts in mean and variance

In statistical process control (SPC), when dealing with a quality characteristic x that is a variable, it is usually necessary to monitor both the mean value and variability. This article proposes an optimization algorithm (called the holistic algorithm) to design the CUSUM charts for this purpose. It facilitates the determination of the charting parameters of the CUSUM charts and considerably or significantly increases their overall detection effectiveness. A single CUSUM chart (called the ABS CUSUM chart) has been developed by the holistic algorithm and fully investigated. This chart is able to detect two-sided mean shifts and increasing variance shifts by inspecting the absolute value of sample mean shift. The results of performance studies show that the overall performance of the ABS CUSUM chart is nearly as good as an optimal 3-CUSUM scheme (a scheme incorporating three individual CUSUM charts). However, since the ABS CUSUM chart is easier for implementation and design, it may be more suitable for many SPC applications in which both mean and variance of a variable have to be monitored.     

Testing for changes in the mean or variance of long memory processes

In this paper, we study the asymptotic CUSUM tests for detecting changes in the mean or variance of a moving-average process with long memory. When there is no change over [O,T], the asymptotic distribution of the test statistic is derived, which allows us to find asymptotic critical values. When there is a change, the behavior of the test statistic is discussed. Conditions for the consistency of these tests are also discussed. Based on the asymptotic results, simulation studies of testing for changes in the mean show that the CUSUM test proposed performs well.     

A Hidden-Markov estimation method for mean-shift detection of fraction defective in production process control

This paper considers a statistical method of estimating mean shift for a fraction defective of population. One traditional method for this estimation problem has been known as the CUSUM (cumulative sum) method, and it provides a method of estimating the occurrence of a shift in the mean from the observed data. We consider this estimation problem of shift occurrence in a production process. It is assumed that the process has two states, one is good (fraction defective low) and the other bad (fraction defective high), and starts in good state with probability one. We are interested in judging when the state has moved to the bad state by analyzing the observed data.In this paper, we model such a phenomenon as a hidden-Markov model. The states which are unobservable in a hidden-Markov model can be analyzed from the sequence of observed results. Hence, the advantage of this modeling technique is that the unknown parameters which are included in the hidden states can be estimated. We compare the performance of this hidden-Markov model with the CUSUM method based on several simulation data sets.     

关于累积和(CUSUM)检验的改进

对连续检验问题，常用的检测方法有三大类其一是众所周知的Shewhartt控制图，它是最常用的对生产过程进行连续监控的控制方法，不过，如果过程均值有小的漂移(即μ-μo小)时，Shewhart控制图的检验效果不是很好，除了Shewhart控制图外，另有二类常用的控制图法，其一是累积和控制图(CUSUM)，由Page^[1]基于似然比导出，其二是指数加权移动平均控制图(EWMA)，由Roberts^[2]给出，它们已被证明在检测小的漂移时效果不错。许多人对CUSUM与EWMA进行了比较，总的来说。最好的CUSUM与最好的EWMA在检测小的漂移方面难分优劣，但CUSUM是由似然比导出的，且其平均运行长度的计算相对来说要简便些，因此，CUSUM在与EWMA的比较中更具优势，应用更广．我们分析了CUSUM的导出过程和公式。指出CUSUM有二个可以进一步改进的方面在此基础上，我们给出了二个新的累积和检验统计量及其判断难则，它们分别是PCUSUM检验统计量Pn和DCUSUM检验统计量Sn．在连续检验问题中判断一个检验方法好坏的最重要的标难是其平均运行长度比较标难是在要求具有相同的受控状态下平均运行长度ARL0的条件下，比较其失控状态下的平均运行长度ARL1，ARL1越小越好我们对PCUSUM检验和DCUSUM检验都建立了其平均运行长度ARL的计算公式．通过对CUSUM，PCUSUM，DCUSUM的平均运行长度的比较我们发现我们提出的新的累积和控制方法确比原来的CUSUM有较大改进。     

多元正态向量均值变点在线监测

在这篇文章中, 我们提出了监测多元独立向量均值变点的两种方法. 第一种方法是构造基于残差的CUSUM统计量; 第二种方法是构造基于递归残差的CUSUM统计量. 并分别得到了对应统计量的渐进分布.模拟表明本文提出的方法监测效果良好, 生产实例也说明该方法具有一定的实用性和有效性.     

CUSUM vs Smoothed-Error Forecast Monitoring Schemes: Some Simulation Results

This paper compares the performance of CUSUM and smoothed-error tracking signals for monitoring the adequacy of exponential smoothing forecasts. Previous research has favoured the CUSUM. However, there is some evidence that the performance of the smoothed-error signal can be improved by a simple modification in its application: the use of different smoothing parameters in the tracking signal and the forecasting model. The effects of this modification are tested using simulated time series. We conclude that the CUSUM is robust to the choice of forecasting parameter, while the smoothed-error signal is not. The CUSUM is also more responsive to small changes in the time series, regardless of the parameters used.     

High moments of two optimal rules of detecting a change in distributions

The CUSUM rule and Shiryayev-Roberts (S-R) rule, proposed by Page and Shiryayev (or Roberts) respectively, are two asymptotically optimal rules of detecting a change in distributions. This paper is concerned with the properties of their moments. In the context of continuous times, explicit formulas of their high moments are established.