Annals of Operations Research

, Volume 192, Issue 1, pp 3–19

LASSO-based multivariate linear profile monitoring


  • Changliang Zou
    • LPMC and Department of Statistics, School of Mathematical SciencesNankai University
  • Xianghui Ning
    • Department of Industrial Engineering and Logistics ManagementHong Kong University of Science and Technology
    • Department of Industrial Engineering and Logistics ManagementHong Kong University of Science and Technology

DOI: 10.1007/s10479-010-0797-8

Cite this article as:
Zou, C., Ning, X. & Tsung, F. Ann Oper Res (2012) 192: 3. doi:10.1007/s10479-010-0797-8


In many applications of manufacturing and service industries, the quality of a process is characterized by the functional relationship between a response variable and one or more explanatory variables. Profile monitoring is for checking the stability of this relationship over time. In some situations, multiple profiles are required in order to model the quality of a product or process effectively. General multivariate linear profile monitoring is particularly useful in practice due to its simplicity and flexibility. However, in such situations, the existing parametric profile monitoring methods suffer from a drawback in that when the profile parameter dimensionality is large, the detection ability of the procedures commonly used T2-type charting statistics is likely to decline substantially. Moreover, it is also challenging to isolate the type of profile parameter change in such high-dimensional circumstances. These issues actually inherit from those of the conventional multivariate control charts. To resolve these issues, this paper develops a new methodology for monitoring general multivariate linear profiles, including the regression coefficients and profile variation. After examining the connection between the parametric profile monitoring and multivariate statistical process control, we propose to apply a variable-selection-based multivariate control scheme to the transformations of estimated profile parameters. Our proposed control chart is capable of determining the shift direction automatically based on observed profile data. Thus, it offers a balanced protection against various profile shifts. Moreover, the proposed control chart provides an easy but quite effective diagnostic aid. A real-data example from the logistics service shows that it performs quite well in the application.


High-dimensionalLeast angle regressionMEWMAProfile monitoringStatistical process controlVariable selection

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© Springer Science+Business Media, LLC 2010