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<article 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" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Discrete and Continuous Models and Applied Computational Science</journal-id><journal-title-group><journal-title xml:lang="en">Discrete and Continuous Models and Applied Computational Science</journal-title><trans-title-group xml:lang="ru"><trans-title>Discrete and Continuous Models and Applied Computational Science</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2658-4670</issn><issn publication-format="electronic">2658-7149</issn><publisher><publisher-name xml:lang="en">Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">17895</article-id><article-id pub-id-type="doi">10.22363/2312-9735-2018-26-1-74-83</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Modeling and Simulation</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Математическое моделирование</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Modeling of Extreme Precipitation Fields on the Territoryof the European Part of Russia</article-title><trans-title-group xml:lang="ru"><trans-title>Моделирование полей экстремальных осадков на территорииЕвропейской части России</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Shchetinin</surname><given-names>E Yu</given-names></name><name xml:lang="ru"><surname>Щетинин</surname><given-names>Е Ю</given-names></name></name-alternatives><bio xml:lang="en"><p>Shchetinin E. Yu. - professor, Doctor of Physical and Mathematical Sciences, Leading Researcher of FGU “All-Russian research institute on problems of civil defence and emergencies of Emergency Control Ministry of Russia”</p></bio><bio xml:lang="ru"><p>Щетинин Евгений Юрьевич - профессор, доктор физико-математических наук, ведущий научный сотрудник ФГБУ ВНИИ ГОЧС (ФЦ)</p></bio><email>riviera-molto@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Rassakhan</surname><given-names>N D</given-names></name><name xml:lang="ru"><surname>Рассахан</surname><given-names>Н Д</given-names></name></name-alternatives><bio xml:lang="en"><p>Rassakhan N. D. - Master of Science of the Applied Mathematics Department, MSTU “Stankin”</p></bio><bio xml:lang="ru"><p>Рассахан Никита Дмитриевич - магистрант кафедры прикладной математики ФГБОУ ВО МГТУ «Станкин»</p></bio><email>rassahan@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">FGU “All-Russian research institute on problems of civil defence and emergencies of Emergency Control Ministry of Russia</institution></aff><aff><institution xml:lang="ru">Всероссийский научно-исследовательский институт по проблемам гражданской обороны и чрезвычайных ситуаций МЧС России</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Moscow State Technology University “STANKIN”</institution></aff><aff><institution xml:lang="ru">Московский государственный технологический университет «Станкин»</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2018-12-15" publication-format="electronic"><day>15</day><month>12</month><year>2018</year></pub-date><volume>26</volume><issue>1</issue><issue-title xml:lang="en">VOL 26, NO1 (2018)</issue-title><issue-title xml:lang="ru">ТОМ 26, №1 (2018)</issue-title><fpage>74</fpage><lpage>83</lpage><history><date date-type="received" iso-8601-date="2018-02-28"><day>28</day><month>02</month><year>2018</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2018, Shchetinin E.Y., Rassakhan N.D.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2018, Щетинин Е.Ю., Рассахан Н.Д.</copyright-statement><copyright-year>2018</copyright-year><copyright-holder xml:lang="en">Shchetinin E.Y., Rassakhan N.D.</copyright-holder><copyright-holder xml:lang="ru">Щетинин Е.Ю., Рассахан Н.Д.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">http://creativecommons.org/licenses/by/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/miph/article/view/17895">https://journals.rudn.ru/miph/article/view/17895</self-uri><abstract xml:lang="en"><p>Present work is devoted to the study and development of space-time statistical structures ofextreme type modeling with the use of the max-stable processes. The theory of one-dimensionalextremal values and its extension to the two-dimensional case are considered and for that max-stable processes are introduced and then the main parametric families of max-stable processes(Schlather, Smith, Brown-Resnick, and Extremal-t) are presented. By modifying the maximumlikelihood method, namely using the paired likelihood function, parameter estimates wereobtained for each of the models whose eﬃciency was compared using the Takeuchi informationcriterion (TIC).Resulting models are coherent with classical extreme value theory and allow consistenttreatment of spatial dependence of rainfall. We illustrate the ideas through data, based ondaily cumulative rainfall totals recorded at 14 stations in central European part of Russia forperiod 1966-2016 years. We compare ﬁts of diﬀerent statistical models appropriate for spatialextremes and select the model that is the best for ﬁtting our data. The method can be used inother situations to produce simulations needed for hydrological models, and in particular forthe generation of spatially heterogeneous extreme rainfall ﬁelds over catchments. It is shownthat the most successful model for the data we studied is the model from the extremal-t familywith the Whittle-Matern correlation function.</p></abstract><trans-abstract xml:lang="ru"><p>В настоящей работе исследована проблема моделирования пространственно-временных статистических структур экстремального типа с использованием процессов устойчивых максимумов. Рассмотрена теория одномерных экстремальных величин и её расширение додвумерного случая, для чего вводятся процессы устойчивых максимумов.Предложена математическая модель процесса устойчивых максимумов и представлены основные параметрические семейства - Шлатера, Смита, Брауна-Резника, Экстремальное-t.При помощи модификации метода максимального правдоподобия, а именно с использованием парной функции правдоподобия, были получены оценки параметров для каждойиз моделей, эффективность которых была затем сравнена при помощи информационного критерия Такеучи (TIC).Полученные модели согласуются с классической теорией экстремальных значений и позволяют рассматривать устойчивую пространственную зависимость осадков. Эффективность предложенных моделей проверялась на ежедневных данных по суммарным осадкам, за-регистрированных на 14 станциях в центральной европейской части России на период1966-2016 гг.: сравниваются статистические модели из различных семейств, подходящих для пространственных экстремумов, после чего выбираются те, которые наилучшим образом описывают существующие данные. Этот метод можно использовать и в других приложениях для создания симуляций, необходимых для гидрологических моделей и, в частности, для создания пространственно-неоднородных осадков над водосборами. Было показано, что наилучшей моделью оказался экстремальный-t процесс с корреляционной функцией Уиттла-Матерна.</p></trans-abstract><kwd-group xml:lang="en"><kwd>spatial modeling</kwd><kwd>extreme rainfall</kwd><kwd>max-stable processes</kwd><kwd>ex-treme value theory</kwd><kwd>spatial structures of statistical dependence</kwd><kwd>pairwise likelihood function</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>пространственное моделирование</kwd><kwd>экстремальные осадки</kwd><kwd>процессыустойчивых максимумов</kwd><kwd>теория экстремальных величин</kwd><kwd>пространственные структурыстатистической зависимости</kwd><kwd>парная функция правдоподобия</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><citation-alternatives><mixed-citation xml:lang="en">V. A. Akimov, A. A. Bykov, E. Y. Shchetinin, Introduction to Extreme Value Statistics and Its Applications, FGU “All-Russian research institute on problems of civil defence and emergencies of Emergency Control Ministry of Russia”, Moscow, 2009, in Russian.</mixed-citation><mixed-citation xml:lang="ru">Акимов В. А., Быков А. А., Щетинин Е. Ю. Введение в статистику экстремальных величин и ее приложения. - М.: ФГУ ВНИИ ГОЧС (ФЦ) МЧС России, 2009.</mixed-citation></citation-alternatives></ref><ref id="B2"><label>2.</label><citation-alternatives><mixed-citation xml:lang="en">L. de Haan, A. Ferraria, Extreme Value Theory: an Introduction, Springer-Verlag, New York, 2006.</mixed-citation><mixed-citation xml:lang="ru">de Haan L., Ferraria A. Extreme Value Theory: an Introduction. - New York: Springer-Verlag, 2006.</mixed-citation></citation-alternatives></ref><ref id="B3"><label>3.</label><citation-alternatives><mixed-citation xml:lang="en">R.-D. Reiss, M. Thomas, Statistical Analysis of Extreme Values with Applications to Insurance, Finance, Hydrology and Other Fields, Birkhauser, Basel, 2007.</mixed-citation><mixed-citation xml:lang="ru">Reiss R.-D., Thomas M. Statistical Analysis of Extreme Values with Applications to Insurance, Finance, Hydrology and Other Fields. - Basel: Birkhauser, 2007.</mixed-citation></citation-alternatives></ref><ref id="B4"><label>4.</label><citation-alternatives><mixed-citation xml:lang="en">B. M. Brown, S. I. Resnick, Extreme Values of Independent Stohastic Processes, Journal of Applied Probability 14 (1977) 732–739.</mixed-citation><mixed-citation xml:lang="ru">Brown B. M., Resnick S. I. Extreme Values of Independent Stohastic Processes // Journal of Applied Probability. - 1977. - Vol. 14. - Pp. 732-739.</mixed-citation></citation-alternatives></ref><ref id="B5"><label>5.</label><citation-alternatives><mixed-citation xml:lang="en">J. Smith, A. Karr, A Statistical Model of Extreme Storm Rainfall, Journal of Geoghysical Research: Atmospheres 95 (1990) 2083–2092.</mixed-citation><mixed-citation xml:lang="ru">Smith J., Karr A. A Statistical Model of Extreme Storm Rainfall // Journal of Geoghysical Research: Atmospheres. - 1990. - Vol. 95. - Pp. 2083-2092.</mixed-citation></citation-alternatives></ref><ref id="B6"><label>6.</label><citation-alternatives><mixed-citation xml:lang="en">M. Schlather, Models for Stationary Max-Stable Random Fields, Extremes 5 (1) (2002) 33–44.</mixed-citation><mixed-citation xml:lang="ru">Schlather M. Models for Stationary Max-Stable Random Fields // Extremes. - 2002. - Vol. 5, No 1. - Pp. 33-44.</mixed-citation></citation-alternatives></ref><ref id="B7"><label>7.</label><citation-alternatives><mixed-citation xml:lang="en">T. Opitz, Extremal t Processes: Elliptical Domain of Attraction and a Spectral Representation, Journal of Multivariate Analysis 122 (2013) 409–413.</mixed-citation><mixed-citation xml:lang="ru">Opitz T. Extremal t Processes: Elliptical Domain of Attraction and a Spectral Representation // Journal of Multivariate Analysis. - 2013. - Vol. 122. - Pp. 409-413.</mixed-citation></citation-alternatives></ref><ref id="B8"><label>8.</label><citation-alternatives><mixed-citation xml:lang="en">A. Aghakouchak, N. Nasrollahi, Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data, Water Resources Management 24 (6) (2010) 1229– 1249.</mixed-citation><mixed-citation xml:lang="ru">Aghakouchak A., Nasrollahi N. Semi-Parametric and Parametric Inference of Extreme Value Models for Rainfall Data // Water Resources Management. - 2010. - Vol. 24, No 6. - Pp. 1229-1249.</mixed-citation></citation-alternatives></ref><ref id="B9"><label>9.</label><citation-alternatives><mixed-citation xml:lang="en">A. C. Davison, S. A. Padoan, M. Ribatet, Statistical Modeling of Spatial Extremes, Statistical Science 27 (2) (2012) 161–186.</mixed-citation><mixed-citation xml:lang="ru">Davison A. C., Padoan S. A., Ribatet M. Statistical Modeling of Spatial Extremes // Statistical Science. - 2012. - Vol. 27, No 2. - Pp. 161-186.</mixed-citation></citation-alternatives></ref><ref id="B10"><label>10.</label><citation-alternatives><mixed-citation xml:lang="en">S. Coles, An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag, London, 2001.</mixed-citation><mixed-citation xml:lang="ru">Coles S. An Introduction to Statistical Modeling of Extreme Values. - London: Springer-Verlag, 2001.</mixed-citation></citation-alternatives></ref><ref id="B11"><label>11.</label><citation-alternatives><mixed-citation xml:lang="en">J. Galambos, Order Statistics of Samples from Multivariate Distributions, Journal of the American Statistical Association 70 (351) (1975) 674–680.</mixed-citation><mixed-citation xml:lang="ru">Galambos J. Order Statistics of Samples from Multivariate Distributions // Journal of the American Statistical Association. - 1975. - Vol. 70, No 351. - Pp. 674-680.</mixed-citation></citation-alternatives></ref><ref id="B12"><label>12.</label><citation-alternatives><mixed-citation xml:lang="en">R. Davis, C. Kluppelberg, C. Steinkohl, Max-Stable Processes for Modeling Extremes Observed in Space and Time, Journal of the Korean Statistical Society 42 (3) (2013) 399–414.</mixed-citation><mixed-citation xml:lang="ru">Davis R., Kluppelberg C., Steinkohl C. Max-Stable Processes for Modeling Extremes Observed in Space and Time // Journal of the Korean Statistical Society. - 2013. - Vol. 42, No 3. - Pp. 399-414.</mixed-citation></citation-alternatives></ref><ref id="B13"><label>13.</label><citation-alternatives><mixed-citation xml:lang="en">P. Embrechts, F. Lindskog, A. McNeil , Modelling Dependence with Copulas and Applications to Risk Management, Elseiver, 2001.</mixed-citation><mixed-citation xml:lang="ru">Embrechts P., Lindskog F., McNeil A. Modelling Dependence with Copulas and Applications to Risk Management. - Elseiver, 2001.</mixed-citation></citation-alternatives></ref><ref id="B14"><label>14.</label><citation-alternatives><mixed-citation xml:lang="en">P. Diggle, P. J. Ribeiro, Model-Based Geostatistics, Springer-Verlag, New York, 2007.</mixed-citation><mixed-citation xml:lang="ru">Diggle P., Ribeiro P. J. Model-Based Geostatistics. - N.-Y.: Springer-Verlag, 2007.</mixed-citation></citation-alternatives></ref><ref id="B15"><label>15.</label><citation-alternatives><mixed-citation xml:lang="en">Z. Kabluchko, M. Schlather, L. de Haan, Stationary Max-Stable Fields Associated to Negative Deﬁnite Functions, The Annals of Probability 37 (5) (2009) 2042–2065.</mixed-citation><mixed-citation xml:lang="ru">Kabluchko Z., Schlather M., de Haan L. Stationary Max-Stable Fields Associated to Negative Deﬁnite Functions // The Annals of Probability. - 2009. - Vol. 37, No 5. - Pp. 2042-2065.</mixed-citation></citation-alternatives></ref><ref id="B16"><label>16.</label><citation-alternatives><mixed-citation xml:lang="en">S. Padoan, M. Ribatet, S. Sisson, Likelihood-Based Inference for Max-Stable Processes, Journal of the American Statistical Association (Theory &amp; Methods) 105 (489) (2010) 263–277.</mixed-citation><mixed-citation xml:lang="ru">Padoan S., Ribatet M., Sisson S. Likelihood-Based Inference for Max-Stable Processes // Journal of the American Statistical Association (Theory &amp; Methods). - 2010. - Vol. 105, No 489. - Pp. 263-277.</mixed-citation></citation-alternatives></ref><ref id="B17"><label>17.</label><citation-alternatives><mixed-citation xml:lang="en">J. Beirlant, Y. Goegebeur, J. Teugels, J. Segers, Statistics of Extremes: Theory and Applications, Wiley, New York, 2004.</mixed-citation><mixed-citation xml:lang="ru">Statistics of Extremes: Theory and Applications / J. Beirlant, Y. Goegebeur, J. Teugels, J. Segers. - New York: Wiley, 2004.</mixed-citation></citation-alternatives></ref><ref id="B18"><label>18.</label><citation-alternatives><mixed-citation xml:lang="en">C. Dombry, F. Eyi-Minko, M. Ribatet, Conditional Simulation of Max-Stable Processes, Biometrika 100 (1) (2013) 111–124.</mixed-citation><mixed-citation xml:lang="ru">Dombry C., Eyi-Minko F., Ribatet M. Conditional Simulation of Max-Stable Processes // Biometrika. - 2013. - Vol. 100, No 1. - Pp. 111-124.</mixed-citation></citation-alternatives></ref><ref id="B19"><label>19.</label><citation-alternatives><mixed-citation xml:lang="en">G. Frahm, M. Junker, R. Schmidt, Estimating the Tail-Dependence Coeﬃcient: Properties and Pitfalls, Insurance: Mathematics and Economics 37 (1) (2005) 80–100.</mixed-citation><mixed-citation xml:lang="ru">Frahm G., Junker M., Schmidt R. Estimating the Tail-Dependence Coeﬃcient: Properties and Pitfalls // Insurance: Mathematics and Economics. - 2005. - Vol. 37, No 1. - Pp. 80-100.</mixed-citation></citation-alternatives></ref><ref id="B20"><label>20.</label><citation-alternatives><mixed-citation xml:lang="en">R. Schmidt, U. Stadtmuller, Non-Parametric Estimation of Tail Dependence, Scandinavian Journal of Statistics 33 (2) (2006) 307–335.</mixed-citation><mixed-citation xml:lang="ru">Schmidt R., Stadtmuller U. Non-Parametric Estimation of Tail Dependence // Scandinavian Journal of Statistics. - 2006. - Vol. 33, No 2. - Pp. 307-335.</mixed-citation></citation-alternatives></ref></ref-list></back></article>
