可变抽样区间和样本容量的非正态EWMA控制图经济设计

为了降低过程控制成本和提高监控效率,针对质量特性值不服从正态分布的情况,研究可变抽样区间和样本容量(VSSI)的指数加权移动平均(EWMA)控制图的经济设计问题。首先对监控非正态分布的EWMA控制图进行抽样区间和样本容量变化设计;其次建立VSSI 非正态EWMA控制图的经济模型,通过使费用成本函数最小得到控制图的最优设计参数组合;然后给出工业中的一个例子,用遗传算法对经济模型搜寻最优解;接下来对经济模型进行灵敏度分析,得出控制图费用参数与设计参数之间的影响关系;最后通过最优性分析,得出所建立的VSSI 非正态EWMA控制图的经济性优于VSI、VSS非正态EWMA控制图。     

Some properties of a simple moving average when applied to forecasting a time series

Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.     

基于预防维修和质量损失函数的VSI EWMA控制图经济设计

为了提高过程监控效率的同时降低过程控制成本,研究可变抽样区间(VSI)指数加权移动平均(EWMA)控制图的经济设计问题。首先建立基于预防维修和质量损失函数的VSI EWMA控制图联合经济模型;使单位时间的损失成本函数最小来确定参数的最优值;其次用遗传算法来寻找联合经济模型的最优解,并给出一个算例。最后对VSI EWMA控制图联合经济模型进行灵敏度分析,得出控制图模型参数对设计参数的影响关系。     

可变抽样区间几何EWMA控制图的经济设计

为了降低生产过程周期成本,本文对单位缺陷数服从几何分布时,可变抽样区间的指数加权移动平均(EWMA)控制图进行经济设计。首先建立可变抽样区间几何EWMA控制图的经济模型,使单位时间期望费用最小来确定参数的最优值;其次用遗传算法来寻找经济模型的最优解;最后对可变抽样区间几何EWMA控制图的经济模型进行灵敏度分析和最优性分析。研究结果表明单位时间期望费用分别随着异常原因发生的频率、过程失控时单位时间的质量费用、发现异常原因的时间期望值和纠正过程的时间期望值的增大而增大。     

Joint economic design of EWMA control charts for mean and variance

Control charts with exponentially weighted moving average (EWMA) statistics (mean and variance) are used to jointly monitor the mean and variance of a process. An EWMA cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria. The pure economic model is extended to the economic-statistical design by adding constraints associated with in-control and out-of-control average run lengths. The quality related production costs are calculated using Taguchi’s quadratic loss function. The optimal values of smoothing constants, sampling interval, sample size, and control chart limits are determined by using a numerical search method. The average run length of the control scheme is computed by using the Markov chain approach. Computational study indicates that optimal sample sizes decrease as the magnitudes of shifts in mean and/or variance increase, and higher values of quality loss coefficient lead to shorter sampling intervals. The sensitivity analysis results regarding the effects of various inputs on the chart parameters provide useful guidelines for designing an EWMA-based process control scheme when there exists an assignable cause generating concurrent changes in process mean and variance.     

可变抽样区间的非正态EWMA图的经济设计

本文主要讨论非正态总体下,可变抽样区间的EWMA图的经济设计问题。首先利用Burr分布近似各种非正态分布,建立可变抽样区间的非正态EWMA图的经济模型,使总期望费用最型小来确定参数的最优值;其次用遗传算法来寻找该经济模型的最优解,并给出工业中的一个例子;最后对可变抽样区间的非正态EWMA图的经济模型进行灵敏度分析。     

Individuals control schemes for monitoring the mean and variance of processes subject to drifts

This paper investigates control chart schemes for detecting drifts in the process mean μ and/or process standard deviation σ when individual observations are sampled. Drifts may be due to causes such as gradual deterioration of equipment, catalyst aging, waste accumulation, or human causes, such as operator fatigue or close supervision. The standard Shewhart X chart and moving range (MR) chart are evaluated, as well as several types of exponentially weighted moving average (EWMA) charts and combinations of charts involving these EWMA charts. We show that the combinations of the EWMA charts detect slow-rate and moderate-rate drifts much faster than the combined X and MR charts. We also show that varying the sampling interval adaptively as a function of the process data results in notable reductions in the detection delay of drifts in μ and/or σ.     

可变抽样区间的二项EWMA控制图

在制造过程中,对产品的不合格品数进行监控时,通常选用计数性控制图-np图,它是基于过程服从二项分布建立的,一般对于过程中出现的较大波动效果明显。为了提高控制图对不合格品数较小波动的监控效果,本文设计了产品不合格品数服从二项分布的EWMA控制图。提出可变抽样区间的二项EWMA控制图,并采用马可夫链法计算其平均报警时间。对固定抽样区间以及可变抽样区间二项EWMA控制图对比研究,表明当过程失控时,可变抽样区间二项EWMA控制图具有较小的失控平均报警时间,能够迅速监测出过程中的异常波动,明显优于固定抽样区间的二项EWMA控制图。     

Robust optimal investment–reinsurance strategies for an insurer with multiple dependent risks

This paper considers a robust optimal investment and reinsurance problem with multiple dependent risks for an Ambiguity-Averse Insurer (AAI), who is uncertain about the model parameters. We assume that the surplus of the insurance company can be allocated to the financial market consisting of one risk-free asset and one risky asset whose price process satisfies square root factor process. Under the objective of maximizing the expected utility of the terminal surplus, by adopting the technique of stochastic control, closed-form expressions of the robust optimal strategy and the corresponding value function are derived. The verification theorem is also provided. Finally, by presenting some numerical examples, the impact of some parameters on the optimal strategy is illustrated and some economic explanations are also given. We find that the robust optimal reinsurance strategies under the generalized mean–variance premium are very different from that under the variance premium principle. In addition, ignoring model uncertainty risk will lead to significant utility loss for the AAI.     

Feedback quality adjustment with Bayesian state‐space models

In this paper we develop a Bayesian procedure for feedback adjustment and control of a single process. We replace the usual exponentially weighted moving average (EWMA) predictor by a predictor of a local level model. The novelty of this approach is that the noise variance ratio (NVR) of the local level model is assumed to change stochastically over time. A multiplicative time series model is used to model the evolution of the NVR and a Bayesian algorithm is developed giving the posterior and predictive distributions for both the process and the NVR. The posterior distribution of the NVR allows the modeller to judge better and evaluate the performance of the model. The proposed algorithm is semi‐conjugate in the sense that it involves conjugate gamma/beta distributions as well as one step of simulation. The algorithm is fast and is found to outperform the EWMA and other methods. An example considering real data from the microelectronic industry illustrates the proposed methodology. Copyright © 2006 John Wiley & Sons, Ltd.     

Robust optimal investment and reinsurance problem for a general insurance company under Heston model

In this paper, we study a robust optimal investment and reinsurance problem for a general insurance company which contains an insurer and a reinsurer. Assume that the claim process described by a Brownian motion with drift, the insurer can purchase proportional reinsurance from the reinsurer. Both the insurer and the reinsurer can invest in a financial market consisting of one risk-free asset and one risky asset whose price process is described by the Heston model. Besides, the general insurance company’s manager will search for a robust optimal investment and reinsurance strategy, since the general insurance company faces model uncertainty and its manager is ambiguity-averse in our assumption. The optimal decision is to maximize the minimal expected exponential utility of the weighted sum of the insurer’s and the reinsurer’s surplus processes. By using techniques of stochastic control theory, we give sufficient conditions under which the closed-form expressions for the robust optimal investment and reinsurance strategies and the corresponding value function are obtained.     

Coupling Importance Sampling and Multilevel Monte Carlo using Sample Average Approximation

In this work, we propose a smart idea to couple importance sampling and Multilevel Monte Carlo (MLMC). We advocate a per level approach with as many importance sampling parameters as the number of levels, which enables us to handle the different levels independently. The search for parameters is carried out using sample average approximation, which basically consists in applying deterministic optimisation techniques to a Monte Carlo approximation rather than resorting to stochastic approximation. Our innovative estimator leads to a robust and efficient procedure reducing both the discretization error (the bias) and the variance for a given computational effort. In the setting of discretized diffusions, we prove that our estimator satisfies a strong law of large numbers and a central limit theorem with optimal limiting variance, in the sense that this is the variance achieved by the best importance sampling measure (among the class of changes we consider), which is however non tractable. Finally, we illustrate the efficiency of our method on several numerical challenges coming from quantitative finance and show that it outperforms the standard MLMC estimator.     

An Application of Double Exponential Smoothing Technique to Quality Control

休哈特控制图和ＳＵＳＵＭ控制图技术，在制造业得到广泛应用．Ｊ．Ｓ．Ｈｕｎｔｅｒ提出用指数加权滑动平均（ＥＷＭＡ）作为质量管理工具．然而，众所周知存在趋势项的情况下，用ＥＷＭＡ预测往往不是过低就是过高．本文的目的是表明利用双指数平滑（ＤＥＳ）技术可以减少此缺点．文中给出一个例子，它表明双指数平滑法比单指数平滑法，能更好地监控对于目标的偏离．     

Discount Weighted Moving Averages

An estimator to replace exponentially weighted moving averages is described and examples of its use are given. It has similar limiting properties but includes the on-line estimation of the forecast error variance as an integral part. The procedures for starting off and intervention are given.     

Stable mixed moving averages

Summary The class of (non-Gaussian) stable moving average processes is extended by introducing an appropriate joint randomization of the filter function and of the stable noise, leading to stable mixed moving averages. Their distribution determines a certain combination of the filter function and the mixing measure, leading to a generalization of a theorem of Kanter (1973) for usual moving averages. Stable mixed moving averages contain sums of independent stable moving averages, are ergodic and are not harmonizable. Also a class of stable mixed moving averages is constructed with the reflection positivity property.Research supported by AFSOR Contract 91-0030Research also supported by ARO DAAL-91-G-0176Research also supported by AFOSR 90-0168Research also supported by ONR N00014-91-J-0277     

自相关过程的改进型EWMA控制图

常规指数加权移动平均(EWMA)控制图的假设前提是观测数据相互独立,但在实际生产过程中,数据相关违背假设条件。本文首先讨论了序列自相关对常规EWMA控制图的影响,结果表明其检测效能降低。因此,重新估计了平稳过程的σz并在此基础上建立了改进型EWMA控制图。然后运用平均链长比较了改进型EWMA控制图与休哈特图和残差控制图,模拟研究说明当过程非强相关且过程均值发生中小偏移条件下。改进型EWMA控制图的检测效果要优于其他两种控制图。最后,通过一个实例验证了该方法的有效性。     

The Variance of Lead-Time Demand

Many short-term forecasting systems are based on exponentially weighted moving averages. It is usual to forecast the cumulative demand over a lead time or production horizon, and to describe this forecast in terms of its mean and variance. When the forecast horizon is fixed, the variance is often taken as the product of the number of periods and the variance per period. This is a serious error and typically underestimates the variance by a factor of about two. This paper details the need for a proper awareness of the correction factors.     

监测均值变动的多重控制图方法

本文证明了当受控平均运行长度充分大时, 多重控制图有两个优点: 一是相比较GLR (广义似然比) 和GEWMA (广义指数权重移动平均)控制图它可以大大降低运算的复杂性; 二是能够较快地监测均值变化的大小. 数值模拟也表明: 多重控制图不仅优于其构成的单个控制图, 而且在监测未知的均值变动方面也优于单个的CUSUM, EWMA, 多重EWMA和GLR控制图.     

Classic Kriging <Emphasis Type="Italic">versus</Emphasis> Kriging with bootstrapping or conditional simulation: classic Kriging’s robust confidence intervals and optimization

Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by ‘efficient global optimization’ (EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input combination. This conclusion is based on bootstrapping and conditional simulation.