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Cynthia A. Lowry

Bio: Cynthia A. Lowry is an academic researcher from Texas Christian University. The author has contributed to research in topics: Control chart & Multivariate statistics. The author has an hindex of 5, co-authored 5 publications receiving 1947 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure.
Abstract: A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.

1,174 citations

Journal ArticleDOI
TL;DR: A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s as mentioned in this paper, where several recent articles that give methods for interpreting an out-of control signal on a multivariate control chart are analyzed and discussed.
Abstract: A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s. Multivariate cumulative sum (CUSUM) control procedures and a multivariate exponentially weighted moving average (EWMA) control chart are reviewed and recommendations are made regarding their use. Several recent articles that give methods for interpreting an out-of-control signal on a multivariate control chart are analyzed and discussed. Other topics such as the use of principal components and regression adjustment of variables in MQC, as well as frequently used approximations in MQC, are discussed.

696 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluate the properties of the run length distributions of the Shewhart R and S-charts using a Markov chain approach and show that the rules commonly used in practice based on the Western Electric Handbook do not have the statistical performance one might expect, and provide the same in-control ARL's as the usual X-chart with runs rules.
Abstract: The Shewhart R–and S–charts are often used to monitor the variability of a quality characteristic of interest. In order to improve the sensitivity of these charting procedures to detecting small shifts in the process standard deviation, runs rules have been suggested. We evaluate the properties of the run length distributions of these charting procedures using a Markov chain approach. We show that the rules commonly used in practice based on the Western Electric Handbook do not have the statistical performance one might expect. Alternative runs rules are provided that provide the same in-control ARL's as the usual X-chart with runs rules. Average run length comparisons are made among various charts for monitoring variability, including cumulative sum charts and exponentially weighted moving average charts. It is shown that detection of decreases in variability is problematic with all of these methods.

103 citations

Journal ArticleDOI
TL;DR: In this article, Monte Carlo simulation methods were used to compare ten nonparametric estimators of quantiles by using properties such as mean square error and mean absolute deviation, and nine distributions were used for the comparisons.
Abstract: Ten nonparametric estimators of quantiles are compared in small samples by Monte Carlo simulation methods. The estimators are compared by using properties such as mean square error and mean absolute deviation. Nine distributions are used for the comparisons and include long-tailed distributions (e.g., Laplace), short-tailed distributions (e.g., uniform), and skewed distributions (e.g., exponential)

80 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper uses a well-known ‘time lag shift’ method to include dynamic behavior in the PCA model and demonstrates the effectiveness of the proposed methodology on the Tennessee Eastman process simulation.

1,299 citations

Journal ArticleDOI
TL;DR: An overview of multivariate statistical methods use for the statistical process control of both continuous and batch multivariate processes and examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on- line monitoring of an industrial batch polymerization reactor.

1,174 citations

Journal ArticleDOI
TL;DR: Multiscale Principal Component Analysis (MSPCA) as mentioned in this paper combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelation of autocorrelated measurements.
Abstract: Multiscale principal-component analysis (MSPCA) combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelate autocorrelated measurements. MSPCA computes the PCA of wavelet coefficients at each scale and then combines the results at relevant scales. Due to its multiscale nature, MSPCA is appropriate for the modeling of data containing contributions from events whose behavior changes over time and frequency. Process monitoring by MSPCA involves combining only those scales where significant events are detected, and is equivalent to adaptively filtering the scores and residuals, and adjusting the detection limits for easiest detection of deterministic changes in the measurements. Approximate decorrelation of wavelet coefficients also makes MSPCA effective for monitoring autocorrelated measurements without matrix augmentation or time-series modeling. In addition to improving the ability to detect deterministic changes, monitoring by MSPCA also simultaneously extracts those features that represent abnormal operation. The superior performance of MSPCA for process monitoring is illustrated by several examples.

812 citations

Journal ArticleDOI
TL;DR: A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s as mentioned in this paper, where several recent articles that give methods for interpreting an out-of control signal on a multivariate control chart are analyzed and discussed.
Abstract: A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s. Multivariate cumulative sum (CUSUM) control procedures and a multivariate exponentially weighted moving average (EWMA) control chart are reviewed and recommendations are made regarding their use. Several recent articles that give methods for interpreting an out-of-control signal on a multivariate control chart are analyzed and discussed. Other topics such as the use of principal components and regression adjustment of variables in MQC, as well as frequently used approximations in MQC, are discussed.

696 citations

Journal ArticleDOI
TL;DR: The data quality problem in the context of supply chain management (SCM) is introduced and methods for monitoring and controlling data quality are proposed and highlighted.

652 citations