Combined application of methods of maximum consistency and anti-robust parameter estimation in the construction of regression models

Objective. The purpose of the study is to solve the problem of calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency between the real and calculated values of the dependent variable in continuous form. Method. The unknown parameters of the model are calculated by reducing the original problem to a linear programming problem. Its solution should not cause computational difficulties due to the significant number of developed effective software tools. Result. The generated linear programming problem has a dimension acceptable for real situations. Conclusion. The results of solving a numerical example indicate the effectiveness of the method proposed in the work for calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency. The final choice of parameter values remains with the model developer.

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Publication Details

Journal
Herald of Dagestan State Technical University Technical Sciences
Published
2024-10-02
DOI
https://doi.org/10.21822/2073-6185-2024-51-3-117-122
Primary Topic
Advanced Statistical Methods and Models
Type
article
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article

Combined application of methods of maximum consistency and anti-robust parameter estimation in the construction of regression models

S. I. Noskov, Yu. A. Bychkov
Herald of Dagestan State Technical University Technical Sciences
Advanced Statistical Methods and Models
article

Combined application of methods of maximum consistency and anti-robust parameter estimation in the construction of regression models

S. I. Noskov, Yu. A. Bychkov
article en

Abstract

Objective. The purpose of the study is to solve the problem of calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency between the real and calculated values of the dependent variable in continuous form. Method. The unknown parameters of the model are calculated by reducing the original problem to a linear programming problem. Its solution should not cause computational difficulties due to the significant number of developed effective software tools. Result. The generated linear programming problem has a dimension acceptable for real situations. Conclusion. The results of solving a numerical example indicate the effectiveness of the method proposed in the work for calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency. The final choice of parameter values remains with the model developer.

Herald of Dagestan State Technical University Technical SciencesVol. 51(3)
Irkutsk State Transport University (RU)
Openalex Percentile: Top 82%
Advanced Statistical Methods and Models
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