Polynomial Maximization Method for Estimation Parameters of Asymmetric Non-Gaussian Moving Average Models
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Abstract
This paper considers the application of the Polynomial Maximization Method to find estimates of the parameters Non-Gaussian Moving Average model. This approach is adaptive and is based on the analysis of higher-order statistics. Case of asymmetry of the distribution of Moving Average processes is considered. It is shown that the asymptotic variance of estimates of the Polynomial Maximization Method (2nd order) analytical expressions that allow finding estimates and analyzing their uncertainty are obtained. This approach can be significantly less than the variance of the classic estimates based on minimize Conditional Sum of Squares or Maximum Likelihood (in Gaussian case). The increase in accuracy depends on the values of the coefficient’s asymmetry and kurtosis of residuals. The results of statistical modeling by the Monte Carlo Method confirm the effectiveness of the proposed approach.
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Keywords
SOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems science, MATHEMATICS::Applied mathematics
Citation
Zabolotnii S. V., Tkachenko O. M., Warsza Z. L. Polynomial Maximization Method for Estimation Parameters of Asymmetric Non-Gaussian Moving Average Models. Key Challenges in Automation, Robotics and Measurement Techniques. AUTOMATION 2023. Lecture Notes in Networks and Systems. 2023. № 630. рр. 223-231. DOI: https://doi.org/10.1007/978-3-031-25844-2_21 [Scopus]