| Issue № 4 |
Methods of ecological investigations |
pdf-version |
The meaning and applicability of kernel methods in environmental research
| Korosov Andrey Victorovich | DSc, professor, Petrozavodsk State University, Petrozavodsk, Lenina st., 33, korosov@psu.karelia.ru |
|
Keywords: sliding window smoothing filtering kernel methods |
Summary: The methods of primary quantitative processing of data series for the targeted identification of significant trends, including smoothing, filling in gaps, and detecting fluctuations in the level of values, are considered. The emphasis is placed on the ideological similarity of processing methods from different fields of knowledge — the use of sliding window technology, in which local processing of initial values and the formation of a number of values with new properties takes place. Such methods include filtering, approximation, kernel methods, etc., which help to get rid of excessive variability and identify stable relationships and dependencies. Examples of processing real data using special functions of the R language environment are given. © Petrozavodsk State University |
Published on: 30 January 2025 | |
References
Bel'skaya E. N. Medvedev A. V. Mihov E. D. Taseyko O. V. Assessment of the environmental situation using nonparametric modeling methods, Ekologiya i promyshlennost' Rossii. 2017. T. 21, No.. 8. P. 54−58. DOI: 10.18412/1816-0395-2017-8-54-58.
Boss V. Lectures on mathematics. Vol. 5: Functional analysis. M.: KomKniga, 2005. 216 p. URL: https://m.eruditor.one/file/1767438/ (data obrascheniya: 08.12.2024).
Buuren v. S, Groothuis-Oudshoorn K. Multivariate Imputation by Chained Equations in R, Journal of Statistical Software. 2011. Vol. 45, issue 3. 67 p. DOI: 10.18637/jss.v045.i03. URL: https://www.jstatsoft.org/article/view/v045i03 (data obrascheniya: 12.11.2024).
Chambers J. M., Cleveland W. S., Kleiner B., Tukey P. A. Graphical Methods for Data Analysis. Boca Raton; London; New York, 2018. 410 p. URL: https://www.taylorfrancis.com/books/mono/10.1201/9781351072304/graphical-methods-data-analysis-chambers (data obrascheniya: 08.12.2024).
Chernen'kiy V. M. Pticyn N. V. The method of nonparametric fuzzy classification in pattern recognition, Vestnik MGTU im. N. E. Baumana. Priborostroenie. 2005. No. 3. P. 49−58. URL: https://vestnikprib.bmstu.ru/catalog/it/hidden/368.html (data obrascheniya: 12.02.2023).
Davydov A. V. Digital signal processing: Thematic lectures. Ekaterinburg: UGGU, IGiG, GIN, Fond elektronnyh dokumentov, 2005. 185 p. URL: https://uchebana5.ru/cont/1318336.html (data obrascheniya: 08.12.2024).
Devis Dzh. P. Statisticheskiy analiz dannyh v geologii. M.: Nedra, 1990. Kn.2. 427 p.
Difference between LOESS and LOWESS, Cross Validated. URL: https://stats.stackexchange.com/questions/161069/difference-between-loess-and-lowess (data obrascheniya: 08.12.2024).
Dinardo J. Nonparametric Density and Regression Estimation, The Journal of Economic Perspectives. 2001. Vol. 15, No. 4. P. 11−29.
Efimov V. M. Galaktionov Yu. K. Shushpanova N. F. Analysis and prediction of time series by the principal component method. Novosibirsk: Nauka, 1988. 71 p. URL: https://pca.narod.ru/EfimovPart2.pdf; https://pca.narod.ru/EfimovPart2.pdf (data obrascheniya: 08.12.2024).
Efimov V. M., Efimov K. V., Kovaleva V. Y. Principal component analysis and its generalizations for any type of sequence (PCA-Seq), Vavilovskii Zhurnal Genetikii Selektsii = Vavilov Journal of Genetics and Breeding. 2019. Vol. 23 (8). P. 1032−1036. DOI: 10.18699/VJ19.584.
Everitt B., Hothorn T. An Introduction to Applied Multivariate Analysis with R. Springer, 2011. 288 p. URL: https://h.twirpx.one/file/569207/; https://www.webpages.uidaho.edu/~stevel/519/An%20Intro%20to%20Applied%20Multi%20Stat%20with%20R%20by%20Everitt%20et%20al.pdf (data obrascheniya: 12.11.2024).
Gonsales R. Vuds R. Eddins S. Digital Image Processing. M.: Tehnosfera, 2012. 1104 p. URL: https://h.twirpx.one/file/489868/; https://studizba.com/show/1246138-1-gonsales-r-vuds-r-cifrovaya-obrabotka.html (data obrascheniya: 12.11.2024).
Gonsales R. Vuds R. Eddins S. Digital image processing. M.: Tehnosfera, 2005. 1072 p. URL: https://h.twirpx.one/file/489868/ (data obrascheniya: 12.11.2024).
Ivanov D. V. Karpov A. S. Kuz'min E. P. Lempickiy V. S. Hropov A. A. Algorithmic foundations of raster machine graphics. M.: Nacional'nyy Otkrytyy Universitet "INTUIT", 2007. 256 p. URL: https://intuit.ru/studies/courses/993/163/info (data obrascheniya: 08.12.2024).
Kemeron E. K. Trivedi P. K. Microeconometrics. Methods and their applications. M.: Izd. dom «Delo» RANHiGS, 2015. Kn. 1. 552 p.; Kn. 2. 664 p. URL: https://bstudy.net/1004356/ekonomika/predislovie#700 (data obrascheniya: 12.11.2024).
Kernel Density Estimation (KDE) and Kernel Regression (KR) in R, Sandipanweb. 2023. URL: https://sandipanweb.wordpress.com/2016/12/31/kernel-denisty-estimation-kde-and-kernel-regression-kr/ (data obrascheniya: 12.11.2024).
Korosov A. V. Ganyushina N. D. Methods for estimating the parameters of thermoregulation of reptiles (on the example of the common viper, Vipera berus L.), Principy ekologii. 2020. No. 4. P. 88−103. DOI: 10.15393/j1.art.2020.11322.
Korosov A. V. Ecological applications of component analysis. Petrozavodsk: Izd-vo PetrGU, 1996. 152 p. URL: https://korosov.narod.ru/083.pdf (data obrascheniya: 08.12.2024).
Korosov A. V. Ecology of the common viper (Vipera berus L.) in the North (facts and models). Petrozavodsk: Izd-vo PetrGU, 2010. 264 p.
Korosov A. V. Workshop on modeling in the R environment for biologists and ecologists. Petrozavodsk: Izd-vo PetrGU, 2024. 35 p. URL: https://h.twirpx.one/file/4182061/ (data obrascheniya: 08.12.2024).
Mastickiy S. E. Shitikov V. K. Statistical analysis and visualization of data using R. M.: DMK Press, 2014. 496 p. URL: http://www.ievbras.ru/ecostat/Kiril/R/MS_2014/MS_2014.pdf (data obrascheniya: 12.02.2021).
Nonparametric Kernel Smoothing Methods for Mixed Data Types, R Documentation. URL: http://127.0.0.1:30972/library/np/html/np-package.html (data obrascheniya: 12.11.2024).
Norkin D. Textbook on machine learning. 2024. URL: https://education.yandex.ru/handbook/ml/article/metricheskiye-metody (data obrascheniya: 08.12.2024).
Otnes R. Enokson L. Applied time series analysis. M.: Mir, 1982. 428 p. URL: https://dsp-book.narod.ru/oten/gl1.pdf (data obrascheniya: 12.11.2024).
Seredkin I. V. Kostyrya A. V. Gudrich D. M. Petrunenko Yu. K. The use of space by brown bears (Ursus arctos) on Sikhote-Alin, Zhurnal Sibirskogo federal'nogo universiteta. Seriya: Biologiya. 2019. 12 (4). P. 366−384. DOI: 10.17516/1997-1389-0308.
Shitikov V. K. Mastickiy S. E. Classification, regression and other Data Mining algorithms using R. 2017. 351 p. URL: https://www.twirpx.org/file/2203014/, https://ranalytics.github.io/data-mining/, https://github.com/ranalytics/data-mining (data obrascheniya: 12.02.2023).
The R Project for Statistical Computing. 2023. URL: https://www.r-project.org/ (data obrascheniya: 26.07.2023).
Varlamov M. S. Methods of data recovery with omissions, Molodezh' i nauka: Sbornik materialov VIII Vserossiyskoy nauchno-tehnicheskoy konferencii studentov, aspirantov i molodyh uchenyh, posvyaschennoy 155-letiyu so dnya rozhdeniya K. E. Ciolkovskogo. Krasnoyarsk: Sibirskiy federal'nyy un-t, 2012. URL: https://elib.sfu-kras.ru/handle/2311/7633 (data obrascheniya: 08.12.2024).
Varlamova L. P. Tursunov H. A. Application of the sliding window method for image processing, Scientific Progress. 2023. Vol. 4, issue 1. P. 151−157. URL: https://cyberleninka.ru/article/n/primenenie-metoda-skolzyaschego-okna-dlya-obrabotki-izobrazheniy (data obrascheniya: 08.12.2024).
Voroncov K. V. Lectures on metric classification algorithm. M.: VC RAN, 2009. 16 p. URL: http://www.ccas.ru/voron/download/MetricAlgs.pdf (data obrascheniya: 08.12.2024).
Voroncov K. V. Lectures on regression recovery algorithms. M.: VC RAN, 2007. 37 p. URL: http://www.ccas.ru/voron/download/Regression.pdf (data obrascheniya: 08.12.2024).
Yane B. Digital image processing. M.: Tehnosfera, 2007. 584 p. URL: https://vk.com/wall-185879208_1399 (data obrascheniya: 12.11.2024).
Yanovskiy L. P. Buhovec A. G. Introduction to econometrics. M.: KnoRus, 2015. 256 p. URL: https://intuit.ru/studies/courses/20842/787/info (data obrascheniya: 08.12.2024).
Zaycev V. A. Maksimova D. A. Smirnov Yu. V. Belotelov N. V. Use of the habitat by the male musk deer (Moschus moschiferus L.) in the central Sikhote-alin, Zoologicheskiy zhurnal. 2021. T. 100, No. 4. P. 462−480. DOI: 10.31857/S0044513421020264.
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