Journal of Quality Technology - January 2018 - ASQ

Journal of Quality Technology - January 2018

Volume 50 ∙ Number 1

ANNOUNCEMENT

SPECIAL FORUM ON JQT 50th Anniversary Research Paper

  • 50 years of the Journal of Quality Technology
    By Willis A. Jensen, Douglas C. Montgomery, Fugee Tsung and Geoffery G. Vining
    In celebration of the 50th anniversary of the Journal of Quality Technology, we take a historical look at the articles that have appeared in the journal. We highlight some of the key articles across the decades and provide some historical analysis on those that have appeared. This analysis illustrates some trends in the quality technology field and gives some insight about its future focus.

DISCUSSIONS

REJOINDER

  • Rejoinder
    By Willis A. Jensen, Douglas C. Montgomery, Fugee Tsung and Geoffery G. Vining

REGULAR RESEARCH PAPERS

  • Process tracking and monitoring based on discrete jumping model
    By Chao Wang and Shiyu Zhou
    The jumping model has been used as an effective tool in tracking and detecting changes for continuous statistics in various applications. In this article, we extend the current jumping model from the continuous case to the discrete case to track and monitor the changes in attribute data.
  • Some perspectives on nonparametric statistical process control
    By Peihua Qiu
    Statistical process control (SPC) charts play a central role in quality control and management. Many conventional SPC charts are designed under the assumption that the related process distribution is normal. In this article, we give some perspectives on issues related to the robustness of conventional SPC charts and to the strengths and limitations of various nonparametric SPC charts.
  • Response modelling approach to robust parameter design methodology using supersaturated designs
    By Kashinath Chatterjee, Krystallenia Drosou, Stelios D. Georgiou and Christos Koukouvino
    In recent years, both robust parameter designs (RPDs) and supersaturated designs (SSDs) have attracted a great deal of attention. In the present article, a combination of the above two techniques is considered. More precisely, we propose a construction of an effective SSD along with an analysis method in order to deal with the significant problem of the robust parameter design methodology (RPDM).
  • Robust split-plot designs for model misspecification
    By Chang-Yun Lin
    Many existing methods for constructing optimal split-plot designs, such as D-optimal or A-optimal designs, focus only on minimizing the variance of the parameter estimates for the fitted model. However, the true model is usually more complicated; hence, the fitted model is often misspecified. If significant effects not included in the model exist, then the estimates could be highly biased. Therefore a good split-plot design should be able to simultaneously control the variance and the bias of the estimates. In this article, I propose a new method for constructing optimal split-plot designs that are robust under model misspecification.
  • Online profile monitoring for surgical outcomes using a weighted score test
    By Liu Liu, Xin Lai, Jian Zhang and Fugee Tsung
    In the past decade, risk-adjusted control charts have been widely used to monitor surgical outcomes. However, most existing approaches focus on monitoring shifts in location parameters and may not be able to detect a scale change that is also likely to occur in surgical data. In this article, we derive a weighted score test statistic to construct an exponentially weighted moving average chart and propose a new charting method to simultaneously monitor location and scale parameters. This new chart may be applied to monitoring surgical performance.
  • Dimension reduction for a multivariate time series process of a regenerative glass furnace
    By Xuan Huang and Davit Khachatryan
    Conventional dimension-reduction methods for multivariate time series have been based on the inherent assumption that the noise is white and therefore uninformative. While this assumption may have been realistic for a number of applications, it is violated in the case of a large industrial furnace that we discuss in this article.
  • A comparison of traditional and maximum likelihood approaches to estimating thermal indices for polymeric materials
    By Caleb B. King, Yimeng Xie, Yili Hong, Jennifer H. Van Mullekom, Stephanie P. DeHart and Patrick A. DeFeo
    Accelerated destructive degradation testing (ADDT) is a widely used technique for long-term material property evaluation. One area of application is in determining the thermal index (TI) of polymeric materials including thermoplastic, thermosetting, and elastomeric materials.

LETTER TO THE EDITOR

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