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HBR Estimates in a Linear Regression Models

Student: Evgenii Savitskii

Supervisor: Elena R. Goryainova

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 9

Year of Graduation: 2017

Nowadays regression analysis is widespreing in all fields of science and technology, when there are a need to restore the connection between two or more variables. The more popular method, OLS (least squares), does not provide an effective estimate of linear regression parameters if there are outliers or clusters of outliers in the data. Therefore, there was a need to create nonparametric methods that allow processing data with minimal information about the distribution. In this paper, we develop and implement a software algorithm for constructing rank evaluation in MATLAB, to model regression with errors that have different distributions, and there are outliers in the factor space. Moreover, the comparative numerical analysis of OLS, R, HBR, LMS-estimations and experiments in comparison with the emissions and clusters from these data were performed.

Full text (added May 28, 2017)

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