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Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Abstract
Control charts are among the most important tools in statistical process control, used to monitor the extent of variation in a process. When a assignable cause deviation is observed in a control chart, identifying the root causes of the change and determining the time at which the deviation began—referred to as the change point—is crucial and impactful. In some statistical process control problems, the quality of a product or the performance of a process is described by the relationship between a response variable and one or more independent variables, known as a profile. In many applications, such as calibration, this relationship is described by a linear profile, while in other situations more complex models, such as Poisson regression profiles, are required. In this paper, the maximum likelihood estimation method is employed to detect change points in Phase II monitoring of Poisson regression profiles, and its performance is evaluated through simulation.
Sharafi,A. , Amin Niri,M. and Amiri,A. (2012). Detection of Change Points in Poisson Regression Profiles with a Linear Trend. Journal of Quality Engineering and Management, 1(1), 39-44.
MLA
Sharafi,A. , , Amin Niri,M. , and Amiri,A. . "Detection of Change Points in Poisson Regression Profiles with a Linear Trend", Journal of Quality Engineering and Management, 1, 1, 2012, 39-44.
HARVARD
Sharafi A., Amin Niri M., Amiri A. (2012). 'Detection of Change Points in Poisson Regression Profiles with a Linear Trend', Journal of Quality Engineering and Management, 1(1), pp. 39-44.
CHICAGO
A. Sharafi, M. Amin Niri and A. Amiri, "Detection of Change Points in Poisson Regression Profiles with a Linear Trend," Journal of Quality Engineering and Management, 1 1 (2012): 39-44,
VANCOUVER
Sharafi A., Amin Niri M., Amiri A. Detection of Change Points in Poisson Regression Profiles with a Linear Trend. J. Qual. Eng. Manag., 2012; 1(1): 39-44.
