L. N. Bol’shev

645 citations
21 papers · 374 indexed · h-index 10
Topics
Bayesian Methods and Mixture Models (5 papers)Statistical Distribution Estimation and Applications (3 papers)Financial Risk and Volatility Modeling (2 papers)
Journals
Mathematics of ComputationTheory of Probability and Its ApplicationsRevue de l Institut International de Statistique / Review of the International Statistical Institute

In The Last Decade

L. N. Bol’shev

18 papers receiving 246 citations

Peers

L. N. Bol’shev
Comparison fields: 5 of 97
  • Statistics and Probability 116
  • Artificial Intelligence 66
  • Management Science and Operations Research 47
  • Statistics, Probability and Uncertainty 34
  • Mechanics of Materials 29
Replace Hermann Witting with:
Hermann Witting Germany
J. D. P. Meldrum United Kingdom
Nikolay V. Smirnov Russia
Volker Mammitzsch Germany
Harald Bergström Sweden
Holger Müller United States
Harry O. Posten United States
Clark R. Givens United States
Pushpa N. Rathie Brazil
A.J. van Reeken Netherlands
L. N. Bol’shev relative to Hermann Witting Germany Hermann Witting's profile →
Citations per field
00.5×5.8×
Hermann Witting · 1×
Citations per year

Countries citing papers authored by L. N. Bol’shev

Since Specialization
Citations

This map shows the geographic impact of L. N. Bol’shev'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 L. N. Bol’shev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites L. N. Bol’shev more than expected).

Fields of papers citing papers by L. N. Bol’shev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by L. N. Bol’shev. 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 L. N. Bol’shev. The network helps show where L. N. Bol’shev may publish in the future.

Co-authorship network of co-authors of L. N. Bol’shev

This figure shows the co-authorship network connecting the top 25 collaborators of L. N. Bol’shev. A scholar is included among the top collaborators of L. N. Bol’shev 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 L. N. Bol’shev. L. N. Bol’shev 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
#WorkIndexed citations
1 10
2 14
3 5
4 1
5 27
6 1
7 2
8 9
9 13
10 10
11
Tables in Mathematical Statistics
189
12 0
13 1
14 29
15 20
16 4
17 3
18 8
19 7
20 15

About L. N. Bol’shev

L. N. Bol’shev is a scholar working on Theoretical Computer Science, Statistics and Probability and Mathematical Physics, having authored 21 papers that have together received 374 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (5 papers), Statistical Distribution Estimation and Applications (3 papers) and Financial Risk and Volatility Modeling (2 papers). The work is most often cited by research in Statistics and Probability (116 citations), Statistics, Probability and Uncertainty (34 citations) and Management Science and Operations Research (47 citations). Frequent co-authors include Nikolay V. Smirnov, В. М. Золотарев and П. И. Кузнецов. Their work appears in journals such as Mathematics of Computation, Theory of Probability and Its Applications and Revue de l Institut International de Statistique / Review of the International Statistical Institute.

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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