Models, systems, networks in economics, engineering, nature and society
Background. The construction of regression models of various complex objects in accordance with the objectives of the study is sometimes accompanied by the need to split the original data sample into subsamples (groups of observations) or the use of the so-called panel data. The purpose of the st...
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2024-11-01
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| Series: | Модели, системы, сети в экономике, технике, природе и обществе |
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| _version_ | 1832579290515898368 |
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| author | S.I. Noskov E.S. Popov |
| author_facet | S.I. Noskov E.S. Popov |
| author_sort | S.I. Noskov |
| collection | DOAJ |
| description | Background. The construction of regression models of various complex objects
in accordance with the objectives of the study is sometimes accompanied by the need to split
the original data sample into subsamples (groups of observations) or the use of the so-called
panel data. The purpose of the study is to modify the method for calculating estimates of the
parameters of a linear regression equation developed earlier by one of the authors, using the
mixed estimation method. Materials and methods. To achieve this goal, the mathematical
apparatus for solving linear and linear Boolean programming problems was used. Results.
This goal is formalized by setting the task of automating the formation of the composition of
the index set of observation numbers to implement the antirobust estimation method within
the framework of the mixed estimation method by solving a linear Boolean programming
problem. Conclusions. The approach described in the work allows us to combine the advantages of the least modulus and anti-robust estimation methods when modeling. A regression model of freight turnover of road transport in the Russian Federation has been constructed that is adequate to the analyzed object. |
| format | Article |
| id | doaj-art-455b4d76ae964036b1e35e27275bd92d |
| institution | Kabale University |
| issn | 2227-8486 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Penza State University Publishing House |
| record_format | Article |
| series | Модели, системы, сети в экономике, технике, природе и обществе |
| spelling | doaj-art-455b4d76ae964036b1e35e27275bd92d2025-01-30T12:24:49ZengPenza State University Publishing HouseМодели, системы, сети в экономике, технике, природе и обществе2227-84862024-11-0139810410.21685/2227-8486-2024-3-8Models, systems, networks in economics, engineering, nature and societyS.I. Noskov0E.S. Popov1Irkutsk State Transport UniversityIrkutsk State Transport UniversityBackground. The construction of regression models of various complex objects in accordance with the objectives of the study is sometimes accompanied by the need to split the original data sample into subsamples (groups of observations) or the use of the so-called panel data. The purpose of the study is to modify the method for calculating estimates of the parameters of a linear regression equation developed earlier by one of the authors, using the mixed estimation method. Materials and methods. To achieve this goal, the mathematical apparatus for solving linear and linear Boolean programming problems was used. Results. This goal is formalized by setting the task of automating the formation of the composition of the index set of observation numbers to implement the antirobust estimation method within the framework of the mixed estimation method by solving a linear Boolean programming problem. Conclusions. The approach described in the work allows us to combine the advantages of the least modulus and anti-robust estimation methods when modeling. A regression model of freight turnover of road transport in the Russian Federation has been constructed that is adequate to the analyzed object. regression modelparameters estimation methodsmixed estimation methodlinear and linear boolean programming problemsroad transport freight turnover |
| spellingShingle | S.I. Noskov E.S. Popov Models, systems, networks in economics, engineering, nature and society Модели, системы, сети в экономике, технике, природе и обществе regression model parameters estimation methods mixed estimation method linear and linear boolean programming problems road transport freight turnover |
| title | Models, systems, networks in economics, engineering, nature and society |
| title_full | Models, systems, networks in economics, engineering, nature and society |
| title_fullStr | Models, systems, networks in economics, engineering, nature and society |
| title_full_unstemmed | Models, systems, networks in economics, engineering, nature and society |
| title_short | Models, systems, networks in economics, engineering, nature and society |
| title_sort | models systems networks in economics engineering nature and society |
| topic | regression model parameters estimation methods mixed estimation method linear and linear boolean programming problems road transport freight turnover |
| work_keys_str_mv | AT sinoskov modelssystemsnetworksineconomicsengineeringnatureandsociety AT espopov modelssystemsnetworksineconomicsengineeringnatureandsociety |