Filtering and smoothing methods for mixed particle count distributions

Steven E. Somerville, Douglas Montgomery, George Runger

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

A particle count data stream is examined. The data are shown to consist of two mixed process distributions, a base Poisson count process and an outlier process. A model is developed that describes the mixed data stream. A smoothing and filtering method, using an exponentially weighted moving average (EWMA) and Poisson probabilities, is developed that separates the two process distributions into a base process and an outlier process. By separating the two distributions, statistical monitoring schemes can be applied to each. A Bernoulli EWMA is introduced for monitoring the outlier process. Average run length (ARL) evaluation is performed using Markov chain methodology, and suggestions for implementation are provided.

Original languageEnglish (US)
Pages (from-to)2991-3013
Number of pages23
JournalInternational Journal of Production Research
Volume40
Issue number13
DOIs
StatePublished - Sep 10 2002

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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