PhD Thesis Defense: “Self-starting methods in Bayesian Statistical Process Control & Monitoring”

PhD candidate: Konstantinos Bourazas

Dissertation tile: “Self-starting methods in Bayesian Statistical Process Control & Monitoring”

Advisor: Panagiotis Tsiamyrtzis 

Abstract

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 permanent shifts. In addition, we present an online change point scheme available for both univariate and 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.

Seminar Link (zoom): https://polimi-it.zoom.us/j/2489452415?pwd=UExYc3Q5S21pSlY0ZEx3eDFlY0pJdz09