Kesar Singh

48 papers receiving 2.3k citations

Peers

Kesar Singh
Comparison fields: 5 of 144
  • Statistics and Probability 2.0k
  • Statistics, Probability and Uncertainty 670
  • Artificial Intelligence 534
  • Management Science and Operations Research 307
  • Finance 300
Replace William E. Strawderman with:
William E. Strawderman United States
William R. Schucany United States
Regina Y. Liu United States
Jayanta K. Ghosh India
Probal Chaudhuri India
Sanat K. Sarkar United States
D. A. S. Fraser Canada
Edward J. Dudewicz United States
Thomas P. Hettmansperger United States
Heleno Bolfarine Brazil
Kesar Singh relative to William E. Strawderman United States William E. Strawderman's profile →
Citations per field
00.5×1.5×1.8×
William E. Strawderman · 1×
Citations per year

Countries citing papers authored by Kesar Singh

Since Specialization
Citations

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

Fields of papers citing papers by Kesar Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kesar Singh. 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 Kesar Singh. The network helps show where Kesar Singh may publish in the future.

Co-authorship network of co-authors of Kesar Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Kesar Singh. A scholar is included among the top collaborators of Kesar Singh 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 Kesar Singh. Kesar Singh 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
ON ONE TERM EDGEWORTH CORRECTION BY EFRON'S BOOTSTRAP
8
2 85
3 20
4 37
5 13
6 57
7 62
8 3
9 19
10 54
11
On Asymptotic Optimality of the Bootstrap
5
12 6
13 157
14 4
15 45
16 1
17 65
18 86
19 2
20 55

About Kesar Singh

Kesar Singh is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Finance, having authored 48 papers that have together received 2.5k indexed citations. Recurring topics across this work include Statistical Methods and Inference (23 papers), Advanced Statistical Methods and Models (20 papers) and Bayesian Methods and Mixture Models (13 papers). The work is most often cited by research in Statistics and Probability (2.0k citations), Statistics, Probability and Uncertainty (670 citations) and Finance (300 citations). Kesar Singh has collaborated with scholars based in United States, India and Netherlands. Frequent co-authors include Regina Y. Liu, Minge Xie, G. Jogesh Babu, Shaw‐Hwa Lo, William E. Strawderman, Arthur B. Yeh, William C. Parr, Malay Ghosh, Cun‐Hui Zhang and Javier Cabrera. Their work appears in journals such as Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B (Statistical Methodology) and The Annals of Statistics.

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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