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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2015, Volume 55, Number 3, Pages 530–544
DOI: https://doi.org/10.7868/S0044466915030047 (Mi zvmmf10179) 		 
This article is cited in 2 scientific papers (total in 2 papers)

Regression model based on convex combinations best correlated with response

A. A. Dokukin, O. V. Senko

Dorodnicyn Computing Center, Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119333, Russia
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DOI: https://doi.org/10.7868/S0044466915030047
Abstract: A new regression method based on constructing optimal convex combinations of simple linear regressions of the least squares method (LSM regressions) built from original regressors is presented. It is shown that, in fact, this regression method is equivalent to a modification of the LSM including the additional requirement of the coincidence of the sign of the regression parameter with that of the correlation coefficient between the corresponding regressor and the response. A method for constructing optimal convex combinations based on the concept of nonexpandable irreducible ensembles is described. Results of experiments comparing the developed method with the known glmnet algorithm are presented, which confirm the efficiency of the former.
Key words: regression, convex correction, regularization.
Received: 10.09.2013
Revised: 29.09.2014
English version:
Computational Mathematics and Mathematical Physics, 2015, Volume 55, Issue 3, Pages 526–539
DOI: https://doi.org/10.1134/S0965542515030045
Bibliographic databases:
Document Type: Article
UDC: 519.7
MSC: Primary 62J05; Secondary 93E24
Language: Russian
Citation: A. A. Dokukin, O. V. Senko, “Regression model based on convex combinations best correlated with response”, Zh. Vychisl. Mat. Mat. Fiz., 55:3 (2015), 530–544; Comput. Math. Math. Phys., 55:3 (2015), 526–539
Citation in format AMSBIB
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    • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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