| چکیده مقاله | Change point estimation is a useful concept in time series models that could be
applied in several fields such as financing, quality control. It helps to decrease
costs of decision making and production by monitoring stock market and production
lines, respectively. In this paper, the maximum likelihood technique is
developed to estimate change point at which the stationary AR(1) model
changes to a nonstationary process. Filtering and smoothing of dynamic linear
model are used to estimate unknown parameters after change point. We also
assume that correlation exists between samples' statistics. Simulation results
show the effectiveness of the proposed estimators to estimate the change point
of stationary. In addition based on Shewhart control chart, filtering has a better
accuracy in comparison to smoothing. A real example is provided to illustrate
the application.
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