Jim E. Griffin

5.3k citations
139 papers · 3.2k · h-index 31

Impact in

    • Statistical Methods and Inference
    • Statistical Methods and Bayesian Inference
  • Finance top 2%
    • Financial Risk and Volatility Modeling

Papers in

Jim E. Griffin

131 papers receiving 3.1k citations

Peers

Jim E. Griffin
Comparison fields: 5 of 161
  • Statistics and Probability 943
  • Finance 453
  • Artificial Intelligence 964
  • Endocrinology, Diabetes and Metabolism 425
  • Management Science and Operations Research 293
Replace Joanna H. Shih with:
Joanna H. Shih United States
Philip J. Brown United Kingdom
Gábor J. Székely Hungary
Gerhard Winkler Austria
Donald A. Pierce United States
Aloïs Kneip Germany
Huixia Wang United States
Arnoldo Frigessi Norway
J. Sunil Rao United States
Xuming He United States
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Citations per year

Countries citing papers authored by Jim E. Griffin

Since Specialization
Citations

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

Fields of papers citing papers by Jim E. Griffin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jim E. Griffin. 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 Jim E. Griffin. The network helps show where Jim E. Griffin may publish in the future.

Co-authors

The 25 scholars most cited alongside Jim E. Griffin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jim E. Griffin Line = papers co-authored together Jim E. Griffin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 139 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010277
2 2006226
3 2009206
4 1976186
5 2012161
6 2007124
7 1991105
8 200479
9 199076
10 199176
11 199365
12 198461
13 200859
14 201156
15 201652
16 201951
17 202151
18 201051
19 198350
20 199648

About Jim E. Griffin

Jim E. Griffin is a scholar working on Artificial Intelligence, Statistics and Probability, Electrical and Electronic Engineering, Aerospace Engineering and Finance, having authored 139 papers that have together received 3.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (47 papers), Statistical Methods and Inference (39 papers), Particle accelerators and beam dynamics (31 papers), Particle Accelerators and Free-Electron Lasers (27 papers), Financial Risk and Volatility Modeling (24 papers), Statistical Methods and Bayesian Inference (18 papers), Superconducting Materials and Applications (16 papers) and Sexual Differentiation and Disorders (11 papers). The work is most often cited by research in Statistics and Probability (943 citations), Finance (453 citations), Artificial Intelligence (964 citations), Endocrinology, Diabetes and Metabolism (425 citations) and Management Science and Operations Research (293 citations). Jim E. Griffin has collaborated with scholars based in United Kingdom, United States and Cyprus. Frequent co-authors include Mark F. J. Steel, Philip J. Brown, Maria Kalli, Jean D. Wilson, Stephen G. Walker, Michael J. McPhaul, Marco Marcelli, David L. Wild, Roel C. A. Oomen and Richard S. Savage. Their work appears in journals such as IEEE Transactions on Nuclear Science, Statistics and Computing, Journal of Clinical Investigation, Journal of Econometrics and Bayesian Analysis.

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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