Change point estimation of a normal process variance with monotonic change
Year: 2012
Type: article
Abstract: When a control chart signals an out-of-control condition, a search begins to identify and eliminate the cause of disturbance. Identification of the time when a change manifests itself into the process, referred to as the change point, can help process engineers to perform root cause analyses effectively. In this paper, a Maximum Likelihood Estimator (MLE) is proposed to estimate the time of a monotonic change in the variance of a normal quality characteristic. Using Monte Carlo simulation, performance of the proposed estimator is studied and comprehensively compared to the existing maximum likelihood estimators for simple step and linear trend changes. This simulation is repeated for a number of monotonic change types, following a signal from a Shewhart S-control chart. Numerical results reveal that the proposed estimator provides appropriate and robust estimation, with regard to the magnitude and type of change.
Source: Scientia Iranica
Authors: R. Noorossana, M. Heydari
Institution: Iran University of Science and Technology
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W1992016679