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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vdgtu</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник Дагестанского государственного технического университета. Технические науки</journal-title><trans-title-group xml:lang="en"><trans-title>Herald of Dagestan State Technical University. Technical Sciences</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2073-6185</issn><issn pub-type="epub">2542-095X</issn><publisher><publisher-name>Daghestan State Technical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21822/2073-6185-2024-51-3-117-122</article-id><article-id custom-type="elpub" pub-id-type="custom">vdgtu-1562</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ И ТЕЛЕКОММУНИКАЦИИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>INFORMATION TECHNOLOGY AND TELECOMMUNICATIONS</subject></subj-group></article-categories><title-group><article-title>Совместное применение методов максимальной согласованности и антиробастного оценивания параметров при построении регрессионных моделей</article-title><trans-title-group xml:lang="en"><trans-title>Combined application of methods of maximum consistency and anti-robust parameter estimation in the construction of regression models</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Носков</surname><given-names>С. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Noskov</surname><given-names>S. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Носков Сергей Иванович, доктор технических наук, профессор, профессор кафедры информационных технологий и защиты информации</p><p>664074, г. Иркутск, ул. Чернышевского, 15 </p></bio><bio xml:lang="en"><p>Sergey I. Noskov, Dr. Sci.(Eng.), Prof., Prof., Department of Information Technologies and Information Security </p><p>15 Chernyshevskogo Str., Irkutsk 664074 </p></bio><email xlink:type="simple">sergey.noskov.57@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бычков</surname><given-names>Ю. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Bychkov</surname><given-names>Yu. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Бычков Юрий Александрович, аспирант кафедры информационных технологий и защиты информации </p><p>664074, г. Иркутск, ул. Чернышевского, 15 </p></bio><bio xml:lang="en"><p>Yuri A. Bychkov, Graduate Student, Department of Information Technologies and Information Security </p><p>15 Chernyshevskogo Str., Irkutsk 664074 </p></bio><email xlink:type="simple">nik24-11@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Иркутский государственный университет путей сообщения</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Irkutsk State Transport University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>02</day><month>10</month><year>2024</year></pub-date><volume>51</volume><issue>3</issue><fpage>117</fpage><lpage>122</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Носков С.И., Бычков Ю.А., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Носков С.И., Бычков Ю.А.</copyright-holder><copyright-holder xml:lang="en">Noskov S.I., Bychkov Y.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vestnik.dgtu.ru/jour/article/view/1562">https://vestnik.dgtu.ru/jour/article/view/1562</self-uri><abstract><p>Цель. Целью исследования является решение задачи вычисления параметров линейной регрессионной модели на основе совместного применения методов антиробастного оценивания и максимальной согласованности между реальными и вычисленными значениями зависимой переменной в непрерывной форме. Метод. Расчет неизвестных параметров модели производится с помощью сведения исходной задачи к задаче линейного программирования. Ее решение не должно вызывать вычислительных трудностей вследствие значительного количества разработанных эффективных программных средств. Результат. Сформированная задача линейного программирования обладает допустимой для реальных ситуаций размерностью. Вывод. Результаты решения численного примера указывают на эффективность предложенного в работе способа вычисления параметров линейной регрессионной модели на основе совместного применения методов антиробастного оценивания и максимальной согласованности. Окончательный выбор значений параметров остается за разработчиком модели.</p></abstract><trans-abstract xml:lang="en"><p>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.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>линейная регрессия</kwd><kwd>идентификация параметров</kwd><kwd>метод наименьших модулей</kwd><kwd>метод антиробастного оценивания</kwd><kwd>метод максимальной согласованности</kwd><kwd>задача линейного программирования</kwd><kwd>альтернативность</kwd></kwd-group><kwd-group xml:lang="en"><kwd>linear regression</kwd><kwd>parameter identification</kwd><kwd>least modulus methods</kwd><kwd>antirobust estimation</kwd><kwd>maximum consistency</kwd><kwd>linear programming problem</kwd><kwd>alternativeness</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Mohammed Jasim Farhan, Ahmed Mahdi Salih. Using Feasible Graphical Lasso Regression Method to Estimate the Parameters of General Linear Regression Model Under High Dimensional Data with Application // Al Kut Journal of Economics and Administrative Sciences 2023, Volume 15, Issue 48, P. 321-331.</mixed-citation><mixed-citation xml:lang="en">Mohammed Jasim Farhan, Ahmed Mahdi Salih. Using Feasible Graphical Lasso Regression Method to Estimate the Parameters of General Linear Regression Model Under High Dimensional Data with Application. Al Kut Journal of Economics and Administrative Sciences. 2023;15(48):321-331.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Zahraa Karim Fahd, Basem Shaliba Muslim. Estimating the parameters of the kink regression model in the presence of a high-dimensional problem with a practical application // Al Kut Journal of Economics and Administrative Sciences 2023, Volume 15, Issue 48, P. 533-560.</mixed-citation><mixed-citation xml:lang="en">Zahraa Karim Fahd, Basem Shaliba Muslim. Estimating the parameters of the kink regression model in the presence of a high-dimensional problem with a practical application. Al Kut Journal of Economics and Administrative Sciences. 2023;15(48):533-560.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Kumar D., Sharma V.K. An Extension of Exponentiated Gamma Distribution: A New Regression Model with Application // Lobachevskii Journal of Mathematics 2022, Volume 43, Issue 9, P. 2525–2543,</mixed-citation><mixed-citation xml:lang="en">Kumar D., Sharma V.K. An Extension of Exponentiated Gamma Distribution: A New Regression Model with Application. Lobachevskii Journal of Mathematics. 2022; 43(9,):2525–2543.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Ekhlass Abdulameer Al-Azzawi, Lekaa Ali Al-Always. Robust Estimation OF The Partial Regression Model Using Wavelet Thresholding // Journal of Economics and Administrative Sciences, 2022 Vol. 28, No. 133, p. 97–113.</mixed-citation><mixed-citation xml:lang="en">Ekhlass Abdulameer Al-Azzawi, Lekaa Ali Al-Always. Robust Estimation OF The Partial Regression Model Using Wavelet Thresholding. Journal of Economics and Administrative Sciences, 2022;28(133): 97–113.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Akram M.N., Amin M., Lukman A.F., Afzal S. Principal component ridge type estimator for the inverse Gaussian regression model // Journal of Statistical Computation and Simulation, 2022, Vol. 92, Is. 10, P. 2060-2089.</mixed-citation><mixed-citation xml:lang="en">Akram M.N., Amin M., Lukman A.F., Afzal S. Principal component ridge type estimator for the inverse Gaussian regression mode. Journal of Statistical Computation and Simulation 2022; 92(10):2060-2089.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Hasibuan D. O., Pau Teku H., Drostela Putri M. F., Setyawan Y. Dwi Bekti R. Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province // Enthusiastic: International Journal of Applied Statistics and Data Science, 2023, Volume 3, Issue 2, P. 189–201.</mixed-citation><mixed-citation xml:lang="en">Hasibuan D. O., Pau Teku H., Drostela Putri M. F., Setyawan Y. Dwi Bekti R. Application of Geographically Weighted Regression Method on the Human Development Index of Central Java Province. Enthusiastic: International Journal of Applied Statistics and Data Science. 2023; 3(2):189–201.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Guzman-Chavez A.D., Vargas-Rodrigue E. Application of a Multiple Regression Model for the Simultaneous Measurement of Refractive Index and Temperature Based on an Interferometric Optical System // Journal of Sensors, 2023, Volume 2023, Special Issue, Article ID 2820062, 14 pages.</mixed-citation><mixed-citation xml:lang="en">Guzman-Chavez A.D., Vargas-Rodrigue E. Application of a Multiple Regression Model for the Simultaneous Measurement of Refractive Index and Temperature Based on an Interferometric Optical System. Journal of Sensors. 2023;2023, Special Issue: 14, Article ID 2820062.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Türkşen Ö. A novel perspective for parameter estimation of seemingly unrelated nonlinear regression // Journal of Applied Statistics, 2021, Volume 48, Issue 13-15: Special Issue: Recent Statistical Methods for Data Analysis, Applied Economics, Business &amp; Finance, P. 2326-2347.</mixed-citation><mixed-citation xml:lang="en">Türksen Ö. A novel perspective for parameter estimation of seemingly unrelated nonlinear regression. Journal of Applied Statistics. 2021;48(13-15): 2326-2347. Special Issue: Recent Statistical Methods for Data Analysis, Applied Economics, Business &amp; Finance.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Ji A., Wei B., Xu L. Robust estimation of panel data regression models and applications // Communications in Statistics - Theory and Methods, 2023, Vol. 52, No. 21, P. 7647-7659.</mixed-citation><mixed-citation xml:lang="en">Ji A., Wei B., Xu L. Robust estimation of panel data regression models and applications. Communications in Statistics - Theory and Methods. 2023; 52( 21):7647-7659.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Frumento P., Salvati N. Parametric Modelling of M-Quantile Regression Coefficient Functions with Application to Small Area Estimation. Journal of the Royal Statistical Society Series A: Statistics in Society, 2020, Volume 183, Issue 1, Pages 229–250.</mixed-citation><mixed-citation xml:lang="en">Frumento P., Salvati N. Parametric Modeling of M-Quantile Regression Coefficient Functions with Application to Small Area Estimation. Journal of the Royal Statistical Society Series A: Statistics in Society. 2020;183(1):229–250.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Айвазян С.А., Енюков И.С, Мешалкий Л.Д. Прикладная статистика. Основы моделирования и первичная обработка данных. –М.: Финансы и статистика, 1983. -472 с.</mixed-citation><mixed-citation xml:lang="en">Ayvazyan S.A., Enyukov I.S., Meshalky L.D. Applied statistics. Basics of modeling and primary data processing. M.: Finance and Statistics. 1983;472. (In Russ)</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Носков С.И., Попов Е.С., Середкин С.П., Тирских В.В., Торопов В.Д. Вариантное регрессионное моделирование производства электроэнергии в Российской Федерации // Вестник Дагестанского государственного технического университета. Технические науки. - 2023. - Т 50. - № 1. - С. 123-129.</mixed-citation><mixed-citation xml:lang="en">Noskov S.I., Popov E.S., Seredkin S.P., Tirskikh V.V., Toropov V.D. Variant regression modeling of electricity production in the Russian Federation. Herald of Daghestan State Technical University. Technical sciences. 2023; 50(1):123-129. (In Russ)</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Носков С.И. Метод максимальной согласованности в регрессионном анализе // Известия Тульского государственного университета. Технические науки. 2021. № 10. С. 380-385.</mixed-citation><mixed-citation xml:lang="en">Noskov S.I. Method of maximum consistency in regression analysis. News of Tula State University. Technical science. 2021. No. 10. P. 380-385. (In Russ)</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Носков С.И. Применение непрерывного критерия согласованности поведения при построении регрессионных моделей // Известия ТулГУ. Технические науки. 2021 № 6. С. 74-78.</mixed-citation><mixed-citation xml:lang="en">Noskov S.I. Application of a continuous criterion for consistency of behavior in the construction of regression models. Izvestia of Tula State University. Technical science. 2021; 6:74-78. (In Russ)</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Свидетельство о государственной регистрации программы для ЭВМ № 2023660087 Российская Федерация. Программа реализации ассоциирования непрерывной формы метода максимальной согласованности с антиробастным оцениванием параметров при построении линейной регрессионной модели: № 2023618290: заявл. 25.04.2023: опубл. 17.05.2023 / С. И. Носков, Ю. А. Бычков; заявитель Федеральное государственное бюджетное образовательное учреждение высшего образования «Иркутский государственный университет путей сообщения».</mixed-citation><mixed-citation xml:lang="en">Certificate of state registration of a computer program No. 2023660087 Russian Federation. Program for implementing the association of the continuous form of the maximum consistency method with antirobust parameter estimation when constructing a linear regression model: No. 2023618290: application. 04/25/2023: publ. 05/17/2023 / S. I. Noskov, Yu. A. Bychkov; applicant Federal State Budgetary Educational Institution of Higher Education "Irkutsk State Transport University".(In Russ)</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Свидетельство о государственной регистрации программы для ЭВМ № 2022681373 Российская Федерация. Программный комплекс регрессионного моделирования методом смешанного оценивания параметров с тремя альтернативными вариантами разбиения обрабатываемой выборки данных на подвыборки: № 2022680260: заявл. 28.10.2022: опубл. 14.11.2022 / С. И. Носков, К. С. Перфильева; заявитель Федеральное государственное бюджетное образовательное учреждение высшего образования «Иркутский государственный университет путей сообщения».</mixed-citation><mixed-citation xml:lang="en">Certificate of state registration of a computer program No. 2022681373 Russian Federation. Software package for regression modeling using the method of mixed parameter estimation with three alternative options for dividing the processed data sample into subsamples: No. 2022680260: application. 10/28/2022: publ. 11/14/2022 / S. I. Noskov, K. S. Perfilyeva; applicant Federal State Budgetary Educational Institution of Higher Education "Irkutsk State Transport University".(In Russ)</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
