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Abstract
An adaptive approach for sequential parameter-change detection and revision of the moving average parameter in the first-order integrated-moving average time series model is presented. Derivation of recursive formulas based on least squares estimation theory is given. Simulation experiments of this study indicate its validity for on-line parameter tracking applications. Practical considerations in implementing the proposed adaptive estimation system and its extensions to higher-order models are discussed.
Notes
Handled by the Applied Probability: Statistics Department.