Synthetic exponential control charts with unknown parameter

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Scopus Citations
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Author(s)

  • Lirong Sun
  • Bing Xing Wang
  • Baocai Guo
  • Min Xie

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2360-2377
Journal / PublicationCommunications in Statistics: Simulation and Computation
Volume47
Issue number8
Online published18 Jul 2017
Publication statusPublished - 2018

Abstract

The existing synthetic exponential control charts are based on the assumption of known in-control parameter. However, the in-control parameter has to be estimated from a Phase I dataset. In this article, we use the exact probability distribution, especially the percentiles, mean, and standard deviation of the conditional average run length (ARL) to evaluate the effect of parameter estimation on the performance of the Phase II synthetic exponential charts. This approach accounts for the variability in the conditional ARL values of the synthetic chart obtained by different practitioners. Since parameter estimation results in more false alarms than expected, we develop an exact method to design the adjusted synthetic charts with desired conditional in-control performance. Results of known and unknown in-control parameter cases show that the control limit of the conforming run length sub-chart of the synthetic chart should be as small as possible.

Research Area(s)

  • Conditional average run length, Exponential distribution, Parameter estimation, Standard deviation of average run length, Synthetic chart

Citation Format(s)

Synthetic exponential control charts with unknown parameter. / Sun, Lirong; Wang, Bing Xing; Guo, Baocai et al.
In: Communications in Statistics: Simulation and Computation, Vol. 47, No. 8, 2018, p. 2360-2377.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review