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2021•AIP conference proceedingsOpen access

Linear regression modeling in monitoring tasks based on the method of least absolute deviations

А. Н. Тырсин, A.A. Azaryan

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Abstract

Algorithm for the exact solution of the problem of estimating the parameters of linear regression models by the least absolute deviations method is described. It is based on the descent through the nodal straight lines. This algorithm significantly outperforms other well-known methods of solving the problem and it can be effectively used in practice. The computational complexity of the descent algorithm through the nodal straight lines is assessed.

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Algorithm for the exact solution of the problem of estimating the parameters of linear regression models by the least absolute deviations method is described. It is based on the descent through the nodal straight lines. This algorithm significantly outperforms other well-known methods of solving the problem and it can be effectively used in practice. The computational complexity of the descent algorithm through the nodal straight lines is assessed.

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Available abstract

Algorithm for the exact solution of the problem of estimating the parameters of linear regression models by the least absolute deviations method is described. It is based on the descent through the nodal straight lines. This algorithm significantly outperforms other well-known methods of solving the problem and it can be effectively used in practice. The computational complexity of the descent algorithm through the nodal straight lines is assessed.

Key concepts: Least absolute deviations, Linear regression, Algorithm, Absolute deviation, Regression, Descent (aeronautics), Computer science, Gradient descent

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