Estimation of Linear Regression Parameters of Symmetric Non-Gaussian Errors by Polynomial Maximization Method

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dc.contributor.authorZabolotnii, Serhii
dc.contributor.authorЗаболотній, Сергій Васильович
dc.contributor.authorWarsza, Zygmunt Lech
dc.contributor.authorTkachenko, Oleksandr
dc.contributor.authorТкаченко, Олександр Миколайович
dc.date.accessioned2025-11-27T14:23:40Z
dc.date.available2025-11-27T14:23:40Z
dc.date.issued2019
dc.description.abstractIn this paper, a new way of estimation of single-factor linear regression parameters of symmetrically distributed non-Gaussian errors is proposed. This new approach is based on the Polynomial Maximization Method (PMM) and uses the description of random variables by higher order statistics (moments and cumulants). Analytic expressions that allow to find estimates and analyze their asymptotic accuracy are obtained for the degree of polynomial S = 3. It is shown that the variance of polynomial estimates can be less than the variance of estimates of the ordinary least squares’ method. The increase of accuracy depends on the values of cumulant coefficients of higher order of the random regression errors. The statistical modeling of the Monte Carlo method has been performed. The results confirm the effectiveness of the proposed approach.
dc.identifier.citationZabolotnii S. V., Warsza Z. L., Tkachenko O. М. Estimation of Linear Regression Parameters of Symmetric Non-Gaussian Errors by Polynomial Maximization Method. Advances in Intelligent Systems and Computing. [Електронне видання]. 2019. № 920. рр. 636-649. DOI: https://doi.org/10.1007/978-3-030-13273-6_59 [Scopus]
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-030-13273-6_59
dc.identifier.urihttps://dr.csbc.edu.ua/handle/123456789/686
dc.publisherSpringer Nature
dc.subjectSOCIAL SCIENCES::Statistics, computer and systems science
dc.titleEstimation of Linear Regression Parameters of Symmetric Non-Gaussian Errors by Polynomial Maximization Method
dc.typeConference paper
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