Alireza Sharafi, Majid Aminnayeri, Amirhossein Amiri, Mohsen Rasouli,
Volume 24, Issue 2 (6-2013)
Abstract
Identification of a real time of a change in a process, when an out-of-control signal is present is significant. This may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. Another popular topic in the Statistical Process Control (SPC) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropriate for discussing the quality of processes or products. One, rather, uses a relationship between a response variable and one or more explanatory variable for this purpose. In this paper, the concept of Maximum Likelihood Estimator (MLE) applied to estimate of the change point in binary profiles, when the type of change is drift. Simulation studies are provided to evaluate the effectiveness of the change point estimator.
Rassoul Noorossana, Mahnam Najafi,
Volume 28, Issue 4 (11-2017)
Abstract
Change point estimation is as an effective method for identifying the time of a change in production and service processes. In most of the statistical quality control literature, it is usually assumed that the quality characteristic of interest is independently and identically distributed over time. It is obvious that this assumption could be easily violated in practice. In this paper, we use maximum likelihood estimation method to estimate when a step change has occurred in a high yield process by allowing a serial correlation between observations. Monte Carlo simulation is used as a vehicle to evaluate performance of the proposed method. Results indicate satisfactory performance for the proposed method.
