			Home About us Contact

Mean Shifts (mean + shift)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	27%

Selected Abstracts

FINITE SAMPLE EFFECTS OF PURE SEASONAL MEAN SHIFTS ON DICKEY,FULLER TESTS: A SIMULATION STUDY

THE MANCHESTER SCHOOL, Issue 5 2008
ARTUR C. B. DA SILVA LOPESArticle first published online: 18 AUG 200
In this paper, it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts,i.e. seasonal structural breaks which affect only the seasonal cycle,really do matter for Dickey,Fuller long-run unit root tests. Both size and power properties are affected by such breaks but using the t -sig method for lag selection induces a stabilizing effect. Although most results are reassuring when the t -sig method is used, some concern with this type of breaks cannot be disregarded. Further evidence on the poor performance of the t -sig method for quarterly time series in standard (no-break) cases is also presented. [source]

A CUSUM scheme with variable sample sizes and sampling intervals for monitoring the process mean and variance

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2007
Zhang Wu
Abstract The adaptive control feature and CUSUM chart are two monitoring schemes that are much more effective than the traditional static Shewhart chart in detecting process shifts in mean and variance. However, the designs and analyses of the adaptive CUSUM chart are mathematically intractable and the operation is very laborious. This article proposes a VSSI WLC scheme, which is a weighted-loss-function-based CUSUM (WLC) scheme using variable sample sizes and sampling intervals (VSSI). This scheme detects the two-sided mean shift and increasing standard deviation shift based on a single statistic WL (the weighted loss function). Most importantly, the VSSI WLC scheme is much easier to operate and design than a VSSI CCC scheme which comprises three individual CUSUM charts (two of them monitoring the increasing and decreasing mean shifts and one monitoring the increasing variance shift). Overall, the VSSI WLC scheme is much more effective than the static &S charts (by 72.36%), the VSSI &S charts (by 30.97%) and the static WLC scheme (by 50.94%) for detection. It is even more effective than the complicated VSSI CCC scheme for most cases. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Optimal Design of VSI ,X Control Charts for Monitoring Correlated Samples

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2005
Yan-Kwang Chen
Abstract This paper develops an economic design of variable sampling interval (VSI),X control charts in which the next sample is taken sooner than usual if there is an indication that the process is off-target. When designing VSI,X control charts, the underlying assumption is that the measurements within a sample are independent. However, there are many practical situations that violate this hypothesis. Accordingly, a cost model combining the multivariate normal distribution model given by Yang and Hancock with Bai and Lee's cost model is proposed to develop the design of VSI charts for correlated data. An evolutionary search method to find the optimal design parameters for this model is presented. Also, we compare VSI and traditional ,X charts with respect to expected cost per unit time, utilizing hypothetical cost and process parameters as well as various correlation coefficients. The results indicate that VSI control charts outperform the traditional control charts for larger mean shift when correlation is present. In addition, there is a difference between the design parameters of VSI charts when correlation is present or absent. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Process monitoring for correlated gamma-distributed data using generalized-linear-model-based control charts

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 6 2003
Duangporn Jearkpaporn
Abstract A model-based scheme is proposed for monitoring multiple gamma-distributed variables. The procedure is based on the deviance residual, which is a likelihood ratio statistic for detecting a mean shift when the shape parameter is assumed to be unchanged and the input and output variables are related in a certain manner. We discuss the distribution of this statistic and the proposed monitoring scheme. An example involving the advance rate of a drill is used to illustrate the implementation of the deviance residual monitoring scheme. Finally, a simulation study is performed to compare the average run length (ARL) performance of the proposed method to the standard Shewhart control chart for individuals. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Least squares estimation and tests of breaks in mean and variance under misspecification

THE ECONOMETRICS JOURNAL, Issue 1 2004
Jean-Yves Pitarakis
Summary In this paper we investigate the consequences of misspecification on the large sample properties of change-point estimators and the validity of tests of the null hypothesis of linearity versus the alternative of a structural break. Specifically this paper concentrates on the interaction of structural breaks in the mean and variance of a time series when either of the two is omitted from the estimation and inference procedures. Our analysis considers the case of a break in mean under omitted-regime-dependent heteroscedasticity and that of a break in variance under an omitted mean shift. The large and finite sample properties of the resulting least-squares-based estimators are investigated and the impact of the two types of misspecification on inferences about the presence or absence of a structural break subsequently analysed. [source]

Monitoring of batch processes through state-space models

AICHE JOURNAL, Issue 6 2004
Jay H. Lee
Abstract The development of a state-space framework for monitoring batch processes that can complement the existing multivariate monitoring methods is presented. A subspace identification method will be used to extract the dynamic and batch-to-batch trends of the process and quality variables from historical operation data in the form of a "lifted" state-space stochastic model. A simple monitoring procedure can be formed around the state and residuals of the model using appropriate scalar statistical metrics. The proposed state-space monitoring framework complements the existing multivariate methods like the multi-way PCA method, in that it allows us to build a more complete statistical representation of batch operations and use it with incoming measurements for early detection of not only large, abrupt changes but also subtle changes. In particular, it is shown to be effective for detecting changes in the batch-to-batch correlation structure, slow drifts, and mean shifts. Such information can be useful in adapting the prediction model for batch-to-batch control. The framework allows for the use of on-line process measurements and/or off-line quality measurements. When both types of measurements are used in model building, one can also use the model to predict the quality variables based on incoming on-line measurements and quality measurements of previous batches. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1198,1210, 2004 [source]

Seasonal Unit Root Tests Under Structural Breaks,

JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2004
Uwe Hassler
C12; C22 Abstract., In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test, as well as an LM variant thereof, are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions, thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte-Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties. [source]

CUSUM charts for detecting special causes in integrated process control

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2010
Marion R. Reynolds Jr
Abstract This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean-squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright © 2009 John Wiley & Sons, Ltd. [source]

An adaptive dimension reduction scheme for monitoring feedback-controlled processes

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2009
Kaibo Wang
Abstract Detecting dynamic mean shifts is particularly important in monitoring feedback-controlled processes in which time-varying shifts are usually observed. When multivariate control charts are being utilized, one way to improve performance is to reduce dimensions. However, it is difficult to identify and remove non-informative variables statically in a process with dynamic shifts, as the contribution of each variable changes continuously over time. In this paper, we propose an adaptive dimension reduction scheme that aims to reduce dimensions of multivariate control charts through online variable evaluation and selection. The resulting chart is expected to keep only informative variables and hence maximize the sensitivity of control charts. Specifically, two sets of projection matrices are presented and dimension reduction is achieved via projecting process vectors into a low-dimensional space. Although developed based on feedback-controlled processes, the proposed scheme can be easily extended to monitor general multivariate applications. Copyright © 2008 John Wiley & Sons, Ltd. [source]