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Bayesian Statistical Process Control: Predictive Control Charts for continuous distributions in the regular exponential family
Bourazas, Konstantinos; Μπουραζάς, Κωνσταντίνος; Athens University of Economics and Business, Department of Statistics; Tsiamyrtzis, Panagiotis
In this thesis, the attention is focused in Statistical Process Control (SPC) with emphasis to phase I data. We will present a new general Bayesian self-starting procedure, which is based on the posterior predictive distribution and its name is Predictive Control Chart (PCC). We will analytically provide the initial assumptions and the construction of PCC. We will focus our attention on illustrations for single future observables of continuous distributions, which are members of the regular exponential family. A simulation study for out of control scenarios is used to evaluate and compare PCC against other sequential methods, either Frequentist or Bayesian, which are described analytically, for independent and normally distributed data. A sensitivity analysis for PCC concludes this thesis.
Self-starting methods in Bayesian statistical process control and monitoring
(2021-10-26) Bourazas, Konstantinos; Μπουραζάς, Κωνσταντίνος; Ntzoufras, Ioannis; Demiris, Nikolaos; Psarakis, Stelios; Capizzi, Giovanna; Colosimo, Bianca Maria; Chakraborti, Subhabrata; Tsiamyrtzis, Panagiotis
In this dissertation, the center of attention is in the research area of Bayesian Statistical Process Control and Monitoring (SPC/M) with emphasis in developing self-starting methods for short horizon data. The aim is in detecting a process disorder as soon as it occurs, controlling the false alarm rate, and providing reliable posterior inference for the unknown parameters. Initially, we will present two general classes of methods for detecting parameter shifts for data that belong to the regular exponential family. The first, named Predictive Control Chart (PCC), focuses on transient shifts (outliers) and the second, named Predictive Ratio CUSUM (PRC), in persistent shifts. In addition, we present an online change point scheme available for both univariate or multivariate data, named Self-starting Shiryaev (3S). It is a generalization of the well-known Shiryaev’s procedure, which will utilize the cumulative posterior probability that a change point has been occurred. An extensive simulation study along with a sensitivity analysis evaluate the performance of the proposed methods and compare them against standard alternatives. Technical details, algorithms and general guidelines for all methods are provided to assist in their implementation, while applications to real data illustrate them in practice.