ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

A Bayesian Self-Starting Method for Online Monitoring of Phase I Data

13 September 2016, 16:00 – 16:20

Abstract

Submitted by
Panagiotis Tsiamyrtzis
Authors
Panagiotis Tsiamyrtzis (Athens University of Economics and Business), Konstantinos Bourazas (Athens University of Economics and Business), Dimitrios Kiagias (University of Sheffield)
Abstract
Standard Statistical Process Control/Monitoring (SPC/M) charts typically use a phase I/II split for calibration and online testing respectively. The phase I data assumed to fulfill certain standards (e.g. being a random sample from the in control distribution) and one can test them in an off-line mode (i.e. only once the phase I data collection is completed). Some frequentist self-starting methods attempt to improve things by performing calibration and testing simultaneously.
In this work a Bayesian alternative is proposed, which utilizes the (usually) available prior distribution to provide a chart based on the predictive distribution of a future observable (Predictive Control Chart – PCC). It is self-starting and performs online monitoring, right after the first observable becomes available. PCC will be presented in its most general form, allowing data of any (discrete or continuous) distribution as long as it is a member of the regular exponential family. We will also establish that PCC generalizes frequentist self-starting methods. Simulations will examine its performance against the frequentist based methods and a real data set will illustrate its use.
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