Directional change-point detection for process control with multivariate categorical data

Authors

  • Jian Li,

    1. Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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    • Jian Li is currently at the School of Management, Xi'an Jiaotong University, Xi'an, Shaanxi, China

  • Fugee Tsung,

    1. Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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  • Changliang Zou

    Corresponding author
    1. LPMC and Department of Statistics, School of Mathematical Sciences, Nankai University, Tianjin, China
    • LPMC and Department of Statistics, School of Mathematical Sciences, Nankai University, Tianjin, China
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Abstract

Most modern processes involve multiple quality characteristics that are all measured on attribute levels, and their overall quality is determined by these characteristics simultaneously. The characteristic factors usually correlate with each other, making multivariate categorical control techniques a must. We study Phase I analysis of multivariate categorical processes (MCPs) to identify the presence of change-points in the reference dataset. A directional change-point detection method based on log-linear models is proposed. The method exploits directional shift information and integrates MCPs into the unified framework of multivariate binomial and multivariate multinomial distributions. A diagnostic scheme for identifying the change-point location and the shift direction is also suggested. Numerical simulations are conducted to demonstrate the detection effectiveness and the diagnostic accuracy.© 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013

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