A. Yu. Zaîtsev
- Statistics and Probability top 1%
- Mathematical Physics top 5%
- Management Science and Operations Research top 5%
- Numerical Analysis top 5%
- Applied Mathematics top 5%
- Co-authors
- Friedrich GötzeDavid M. MasonEvarist GinéА. И. ЗайцевT. V. ArakG. CevolaniL. SabaS. Righini
- Topics
- Mathematical Approximation and Integration (22 papers)Probability and Risk Models (19 papers)advanced mathematical theories (12 papers)
- Partner nations
- RussiaGermanyUnited States
In The Last Decade
A. Yu. Zaîtsev
56 papers receiving 441 citations
Peers
Comparison fields: 5 of 53
- Statistics and Probability 264
- Mathematical Physics 225
- Management Science and Operations Research 156
- Numerical Analysis 136
- Applied Mathematics 115
Countries citing papers authored by A. Yu. Zaîtsev
This map shows the geographic impact of A. Yu. Zaîtsev'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 A. Yu. Zaîtsev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Yu. Zaîtsev more than expected).
Fields of papers citing papers by A. Yu. Zaîtsev
This network shows the impact of papers produced by A. Yu. Zaîtsev. 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 A. Yu. Zaîtsev. The network helps show where A. Yu. Zaîtsev may publish in the future.
Co-authorship network of co-authors of A. Yu. Zaîtsev
This figure shows the co-authorship network connecting the top 25 collaborators of A. Yu. Zaîtsev. A scholar is included among the top collaborators of A. Yu. Zaîtsev 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 A. Yu. Zaîtsev. A. Yu. Zaîtsev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 0 | |
| 6 | 7 | |
| 7 | 13 | |
| 8 | 30 | |
| 9 | On the strong gaussian approximation in multidimensional case | 2 |
| 10 | 11 | |
| 11 | 3 | |
| 12 | 41 | |
| 13 | 4 | |
| 14 | 10 | |
| 15 | 9 | |
| 16 | 16 | |
| 17 | 22 | |
| 18 | 3 | |
| 19 | 24 | |
| 20 | On the approximation of distributions of sums of independent random vectors by infinitely divisible distributions | 5 |
About A. Yu. Zaîtsev
A. Yu. Zaîtsev is a scholar working on Numerical Analysis, Mathematical Physics and Statistics and Probability, having authored 65 papers that have together received 589 indexed citations. Recurring topics across this work include Mathematical Approximation and Integration (22 papers), Probability and Risk Models (19 papers) and advanced mathematical theories (12 papers). The work is most often cited by research in Statistics and Probability (264 citations), Numerical Analysis (136 citations) and Mathematical Physics (225 citations). A. Yu. Zaîtsev has collaborated with scholars based in Russia, Germany and United States. Frequent co-authors include Friedrich Götze, David M. Mason, Evarist Giné, А. И. Зайцев, T. V. Arak, G. Cevolani, L. Saba, S. Righini, Marco Delbó and Yu. I. Golovko. Their work appears in journals such as Applied Physics Letters, Planetary and Space Science and The Annals of Probability.
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.