Login / Signup

A distribution-free EWMA control chart for monitoring time-between-events-and-amplitude data.

Shu WuPhilippe CastagliolaGiovanni Celano
Published in: Journal of applied statistics (2020)
Many control charts have been developed for the simultaneous monitoring of the time interval T between successive occurrences of an event E and its magnitude X . All these TBEA (Time Between Events and Amplitude) control charts assume a known distribution for the random variables T and X . But, in practice, as it is rather difficult to know their actual distributions, proposing a distribution free approach could be a way to overcome this 'distribution choice' dilemma. For this reason, we propose in this paper a distribution free upper-sided EWMA (Exponentially Weighted Moving Average) type control chart, for simultaneously monitoring the time interval T and the magnitude X of an event. In order to investigate the performance of this control chart and obtain its run length properties, we also develop a specific method called 'continuousify' which, coupled with a classical Markov chain technique, allows to obtain reliable and replicable results. A numerical comparison shows that our distribution-free EWMA TBEA chart performs as the parametric Shewhart TBEA chart, but without the need to pre-specify any distribution. An illustrative example obtained from a French forest fire database is also provided to show the implementation of the proposed EWMA TBEA control chart.
Keyphrases
  • primary care
  • healthcare
  • emergency department
  • climate change
  • magnetic resonance imaging
  • big data
  • deep learning
  • contrast enhanced