Geodesic
Analysis of algorithms for stable estimation of coefficients of multiple linear regression models
Journal of computational and engineering mathematics, Tome 5 (2018) no. 3, pp. 17-23

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Computational experiments on model data were performed in order to study the effectiveness of the algorithms for realization of the least absolute deviations (LAD) method and the generalized method of the least absolute deviations (GLAD) when estimating the parameters of multiple linear regression models based on descent through the nodal straight lines. In addition, a comparative analysis of the algorithms of descent through nodal straight lines for LAD and GLAD with known exact and approximate methods to solve tasks (2) and (3) was carried out.
Keywords: linear regression model, the least absolute deviations method, generalized, computational complexity, comparative analysis. 
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     author = {A. A. Azaryan},
     title = {Analysis of algorithms for stable estimation of coefficients of multiple linear regression models},
     journal = {Journal of computational and engineering mathematics},
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     publisher = {mathdoc},
     volume = {5},
     number = {3},
     year = {2018},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JCEM_2018_5_3_a1/}
}
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A. A. Azaryan. Analysis of algorithms for stable estimation of coefficients of multiple linear regression models. Journal of computational and engineering mathematics, Tome 5 (2018) no. 3, pp. 17-23. http://geodesic.mathdoc.fr/item/JCEM_2018_5_3_a1/