Jun‐ya Gotoh

1.2k citations
39 papers · 774 · h-index 13

Impact in

Papers in

Jun‐ya Gotoh

36 papers receiving 735 citations

Peers

Jun‐ya Gotoh
Comparison fields: 5 of 71
  • Management Science and Operations Research 380
  • Finance 207
  • Management Information Systems 175
  • Numerical Analysis 100
  • Statistics and Probability 87
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C. Lucas United Kingdom
Huifu Xu United Kingdom
José H. Dulá United States
Majid Soleimani-damaneh Iran
Jianjun Gao China
Aparna Mehra India
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Citations per year

Countries citing papers authored by Jun‐ya Gotoh

Since Specialization
Citations

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

Fields of papers citing papers by Jun‐ya Gotoh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun‐ya Gotoh. 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 Jun‐ya Gotoh. The network helps show where Jun‐ya Gotoh may publish in the future.

Co-authors

The 15 scholars most cited alongside Jun‐ya Gotoh, 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 Jun‐ya Gotoh Line = papers co-authored together Jun‐ya Gotoh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006207
2 2017109
3 200051
4 201247
5 201847
6 200146
7 201142
8 201331
9 200221
10
A linear classification model based on conditional geometric score
200417
11 201316
12 201515
13 201615
14 201112
15 200210
16 20109
17 20179
18
Numerical exploration of dynamic behavior of the ornstein-uhlenbeck process via ehrenfest process approximation
20036
19 20186
20 20066

About Jun‐ya Gotoh

Jun‐ya Gotoh is a scholar working on Management Science and Operations Research, Finance, Statistics and Probability, Numerical Analysis and Computational Mechanics, having authored 39 papers that have together received 774 indexed citations. Recurring topics across this work include Risk and Portfolio Optimization (24 papers), Stochastic processes and financial applications (12 papers), Advanced Optimization Algorithms Research (7 papers), Financial Markets and Investment Strategies (6 papers), Sparse and Compressive Sensing Techniques (6 papers), Fuzzy Systems and Optimization (5 papers), Statistical Methods and Inference (5 papers) and Reservoir Engineering and Simulation Methods (4 papers). The work is most often cited by research in Management Science and Operations Research (380 citations), Finance (207 citations), Management Information Systems (175 citations), Numerical Analysis (100 citations) and Statistics and Probability (87 citations). Jun‐ya Gotoh has collaborated with scholars based in Japan, United States and Canada. Frequent co-authors include Yuichi Takano, Akiko Takeda, Hiroshi Konno, Andrew E. B. Lim, Michael Jong Kim, Stan Uryasev, Yoshinobu Kawahara, Mahesan Niranjan, Hui Jin and Ushio Sumita. Their work appears in journals such as European Journal of Operational Research, Annals of Operations Research, Computational Optimization and Applications, Computational Management Science and Management Science.

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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