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- Type of Document: Ph.D. Dissertation
- Language: Farsi
- Document No: 44576 (01)
- University: Sharif University of Technology
- Department: Industrial Engineering
- Advisor(s): Akhavan Niaki, Taghi
- Abstract:
- Knowing the time of change would narrow the search to find and identify the variables disturbing a process. Having this information, an appropriate corrective action could be implemented and valuable time could be saved. Multistage processes that are often observed in current manufacturing processes must be monitored to assure quality products. The change-point detection of such processes has not been proposes investigated yet. Thus, this dissertation proposes maximum likelihood step-change estimators of two kinds of these processes. First, a multistage process with variable quality characteristics is considered and formulated by the first-order auto-regressive model. For the location parameter of this process, the maximum likelihood estimators are obtained by maximizing the likelihood functions. Second, a multistage process with attribute quality characteristics is modeled using a first-order integer-valued auto-regressive time-series (INAR(1)) and the maximum likelihood method is employed to estimate the out-of-control sample along with the out-of-control stage. Besides, the accuracy and the precision of the proposed estimators are examined through some Monte Carlo simulation experiments. The results show that the estimators are accurate and promising
- Keywords:
- Change Point ; Maximum Likelihood Estimation ; Time Series ; Multistage Process ; Count Processes
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