Michael G. Akritas

4.3k citations
105 papers · 2.9k indexed · 1 hit paper · h-index 24
Topics
Statistical Methods and Inference (59 papers)Advanced Statistical Methods and Models (54 papers)Statistical Methods and Bayesian Inference (31 papers)

In The Last Decade

Michael G. Akritas

101 papers receiving 2.7k citations

Hit Papers

Linear regression in astronomy.19902026200220141990200400600

Peers

Michael G. Akritas
Comparison fields: 5 of 176
  • Statistics and Probability 1.5k
  • Astronomy and Astrophysics 623
  • Management Science and Operations Research 515
  • Artificial Intelligence 264
  • Nuclear and High Energy Physics 151
Replace Elizabeth L. Scott with:
Elizabeth L. Scott United States
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Stéphane Girard France
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Michael G. Akritas relative to Elizabeth L. Scott United States Elizabeth L. Scott's profile →
Citations per field
00.5×4.4×
Elizabeth L. Scott · 1×
Citations per year

Countries citing papers authored by Michael G. Akritas

Since Specialization
Citations

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

Fields of papers citing papers by Michael G. Akritas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael G. Akritas. 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 Michael G. Akritas. The network helps show where Michael G. Akritas may publish in the future.

Co-authorship network of co-authors of Michael G. Akritas

This figure shows the co-authorship network connecting the top 25 collaborators of Michael G. Akritas. A scholar is included among the top collaborators of Michael G. Akritas 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 Michael G. Akritas. Michael G. Akritas 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
Probability & Statistics with R for Engineers and Scientists
12
2 14
3 4
4 7
5 9
6 11
7 11
8 7
9
Estimation of the bivariate and marginal distributions with censored data
2
10 17
11 12
12 2
13 154
14 3
15 56
16 2
17 143
18 47
19 11
20 3

About Michael G. Akritas

Michael G. Akritas is a scholar working on Statistics and Probability, Management Science and Operations Research and Statistics, Probability and Uncertainty, having authored 105 papers that have together received 2.9k indexed citations. Recurring topics across this work include Statistical Methods and Inference (59 papers), Advanced Statistical Methods and Models (54 papers) and Statistical Methods and Bayesian Inference (31 papers). The work is most often cited by research in Statistics and Probability (1.5k citations), Management Science and Operations Research (515 citations) and Astronomy and Astrophysics (623 citations). Michael G. Akritas has collaborated with scholars based in United States, Germany and Greece. Frequent co-authors include Steven F. Arnold, Eric D. Feigelson, G. Jogesh Babu, Ingrid Van Keilegom, Edgar Brunner, Michael P. LaValley, J. Siebert, Susan A. Murphy, Nickos Papadatos and Lan Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and The Astrophysical Journal.

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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