Change Point Estimation of Multivariate Multiple Linear Profiles, under Multiple Linear Drifts and Step Changes

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Karimi, Samira | 2016

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 48571 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Mahlooji, Hashem
Abstract:
Control charts are the most popular Statistical Process Control tools used to monitor process changes. However, they are not capable of identifying the real time of a process change, which is essential for diagnosing assignable causes of the change. Therefore, a number of methods of change-point estimation have been developed. In the literature, relatively little study has been done on multiple changes. In this research, a new method based on Maximum Likelihood Estimator (MLE) is introduced to identify linear drifts and step changes in multivariate multiple linear profiles. Due to the massive increase in the amount and time of the calculations along with the growth of the number of the changes, we propose using Genetic Algorithm to purposefully move towards the point with the highest MLE. Finally, this method was tested by conducting a simulation study, the results of which show a satisfactory performance and precision for linear drifts. Concerning step changes, for very small changes the method was unable to properly perform, but the performance and precision improve remarkably for bigger amounts of change
Keywords:
Estimation ; Change Point ; Profiles ; Genetic Algorithm ; Multivariate Methods ; Maximum Likelihood Estimation ; Linear Change Point ; Step Change Point

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