Georgy Shevlyakov

849 total citations
51 papers, 506 citations indexed

About

Georgy Shevlyakov is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Georgy Shevlyakov has authored 51 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics and Probability, 16 papers in Artificial Intelligence and 15 papers in Statistics, Probability and Uncertainty. Recurrent topics in Georgy Shevlyakov's work include Advanced Statistical Methods and Models (21 papers), Advanced Statistical Process Monitoring (15 papers) and Statistical Methods and Inference (11 papers). Georgy Shevlyakov is often cited by papers focused on Advanced Statistical Methods and Models (21 papers), Advanced Statistical Process Monitoring (15 papers) and Statistical Methods and Inference (11 papers). Georgy Shevlyakov collaborates with scholars based in Russia, South Korea and France. Georgy Shevlyakov's co-authors include Kiseon Kim, Hannu Oja, Vladimir Shin, Stephan Morgenthaler, Konstantin Avrachenkov, Yo‐Sung Ho, Choudur Lakshminarayan, Kihyeun Kim, Pankaj Mehra and Alexander Ulanov and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Theory and IEEE Transactions on Wireless Communications.

In The Last Decade

Georgy Shevlyakov

49 papers receiving 478 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Georgy Shevlyakov Russia 13 134 114 112 74 57 51 506
Nikolas Kantas United Kingdom 11 120 0.9× 69 0.6× 246 2.2× 65 0.9× 68 1.2× 33 461
Pascal Bondon France 14 86 0.6× 39 0.3× 132 1.2× 137 1.9× 123 2.2× 59 672
Leonid G. Khachiyan United States 8 40 0.3× 104 0.9× 104 0.9× 42 0.6× 47 0.8× 8 506
A. G. Miamee United States 12 131 1.0× 39 0.3× 106 0.9× 24 0.3× 132 2.3× 39 610
B. La Scala Australia 11 257 1.9× 127 1.1× 449 4.0× 56 0.8× 65 1.1× 19 818
Alessio Benavoli Switzerland 15 78 0.6× 156 1.4× 491 4.4× 55 0.7× 157 2.8× 72 754
Madeleine Udell United States 15 44 0.3× 62 0.5× 194 1.7× 78 1.1× 40 0.7× 41 628
Vladislav B. Tadić United Kingdom 12 109 0.8× 108 0.9× 484 4.3× 74 1.0× 287 5.0× 43 784
Jacek Leśkow Poland 15 72 0.5× 32 0.3× 99 0.9× 37 0.5× 170 3.0× 42 501
Donald R. Barr United States 13 226 1.7× 44 0.4× 92 0.8× 37 0.5× 36 0.6× 38 557

Countries citing papers authored by Georgy Shevlyakov

Since Specialization
Citations

This map shows the geographic impact of Georgy Shevlyakov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Georgy Shevlyakov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georgy Shevlyakov more than expected).

Fields of papers citing papers by Georgy Shevlyakov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Georgy Shevlyakov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Georgy Shevlyakov. The network helps show where Georgy Shevlyakov may publish in the future.

Co-authorship network of co-authors of Georgy Shevlyakov

This figure shows the co-authorship network connecting the top 25 collaborators of Georgy Shevlyakov. A scholar is included among the top collaborators of Georgy Shevlyakov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Georgy Shevlyakov. Georgy Shevlyakov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shevlyakov, Georgy. (2021). Highly Efficient Robust and Stable M-Estimates of Location. Mathematics. 9(1). 105–105. 3 indexed citations
2.
Shevlyakov, Georgy, et al.. (2020). On outlier detection with the Chebyshev type inequalities. SHILAP Revista de lepidopterología. 28–35. 1 indexed citations
3.
Shevlyakov, Georgy, et al.. (2020). Robust Minimax Variance Estimation of Location under Bounded Distribution Interquantile Ranges. Journal of Mathematical Sciences. 248(1). 25–32. 1 indexed citations
4.
Shevlyakov, Georgy, et al.. (2020). Stream Data Preprocessing: Outlier Detection Based on the Chebyshev Inequality with Applications. SHILAP Revista de lepidopterología. 402–407. 9 indexed citations
5.
Shevlyakov, Georgy, et al.. (2019). A Comparative Study of Robust and Stable Estimates of Multivariate Location. Journal of Mathematical Sciences. 237(6). 831–845. 1 indexed citations
6.
Kim, Kiseon, et al.. (2019). Editorial for Special Issue: Underwater Acoustics, Communications, and Information Processing. Applied Sciences. 9(22). 4873–4873. 3 indexed citations
7.
Shevlyakov, Georgy, et al.. (2017). A Modification of Linfoot's Informational Correlation Coefficient. Austrian Journal of Statistics. 46(3-4). 99–105. 1 indexed citations
8.
Shevlyakov, Georgy, et al.. (2016). Robust Estimation of the Correlation Coefficient: An Attempt of Survey. SHILAP Revista de lepidopterología. 57 indexed citations
9.
Shevlyakov, Georgy & Hannu Oja. (2016). Robust Correlation. Wiley series in probability and statistics. 32 indexed citations
10.
Shevlyakov, Georgy, et al.. (2013). Asymptotically stable detection of a weak signal. International Journal of Adaptive Control and Signal Processing. 28(9). 848–858. 1 indexed citations
11.
Shevlyakov, Georgy, et al.. (2012). Asymptotically minimax bias estimation of the correlation coefficient for bivariate independent component distributions. Journal of Multivariate Analysis. 111. 59–65. 10 indexed citations
12.
Ulanov, Alexander, et al.. (2011). Enhancing Accuracy of Multilabel Classification by Extracting Hierarchies. 99. 203–207. 2 indexed citations
13.
Shevlyakov, Georgy, et al.. (2010). Alternative To M-Estimates In Multisensor Data Fusion. Zenodo (CERN European Organization for Nuclear Research). 4(10). 1623–1627. 1 indexed citations
14.
Shevlyakov, Georgy, Jae Won Lee, Kyung Min Lee, Vladimir Shin, & Kiseon Kim. (2009). Robust detection of a weak signal with redescending M‐estimators: A comparative study. International Journal of Adaptive Control and Signal Processing. 24(1). 33–40. 3 indexed citations
15.
Kim, Kiseon, et al.. (2009). Fusion of Decisions Modeled as Weak Signals in Wireless Sensor Networks. 27. 1–5. 5 indexed citations
16.
Kim, Du Yong, Vladimir Shin, Georgy Shevlyakov, & Kiseon Kim. (2007). A design of optimal receding horizon filter for linear systems with uncertainty. 1–4. 1 indexed citations
17.
Avrachenkov, Konstantin, et al.. (2003). Priority queueing with finite buffer size and randomized push-out mechanism. 2 indexed citations
18.
Avrachenkov, Konstantin, et al.. (2003). Priority queueing with finite buffer size and randomized push-out mechanism. 324–325. 11 indexed citations
19.
Shevlyakov, Georgy, et al.. (2002). On a Nonparametric Robust Method of Detection of Signals. Journal of Mathematical Sciences. 111(6). 3879–3887. 1 indexed citations
20.
Shevlyakov, Georgy, et al.. (2002). Minimax variance estimation of a correlation coefficient for ε-contaminated bivariate normal distributions. Statistics & Probability Letters. 57(1). 91–100. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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