Byeong U. Park

4.7k citations
97 papers · 3.0k · h-index 28

Impact in

Papers in

Byeong U. Park

92 papers receiving 2.8k citations

Peers

Byeong U. Park
Comparison fields: 5 of 160
  • Statistics and Probability 1.3k
  • Management Science and Operations Research 984
  • General Economics, Econometrics and Finance 335
  • Economics and Econometrics 850
  • Finance 260
Replace Yuhong Yang with:
Yuhong Yang United States
Aloïs Kneip Germany
Daniel Peña Spain
Enno Mammen Germany
Peter Hall Australia
Xinyu Zhang China
Hrishikesh D. Vinod United States
Zongwu Cai United States
Michael S. Smith Australia
Stephen Portnoy United States
Byeong U. Park relative to Yuhong Yang United States Yuhong Yang's profile →
Citations per field
00.5×1.5×2.2×
Yuhong Yang · 1×
Citations per year

Countries citing papers authored by Byeong U. Park

Since Specialization
Citations

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

Fields of papers citing papers by Byeong U. Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Byeong U. Park. 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 Byeong U. Park. The network helps show where Byeong U. Park may publish in the future.

Co-authors

The 25 scholars most cited alongside Byeong U. Park, 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 Byeong U. Park Line = papers co-authored together Byeong U. Park links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 97 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1990345
2 1998279
3 2008221
4 1992163
5 2006158
6 1999139
7 2013125
8 2008103
9 1990102
10 199485
11 201376
12 199865
13 200962
14 199461
15 199845
16 200245
17 201044
18 201240
19 199940
20 201038

About Byeong U. Park

Byeong U. Park is a scholar working on Statistics and Probability, Artificial Intelligence, Management Science and Operations Research, Control and Systems Engineering and Economics and Econometrics, having authored 97 papers that have together received 3.0k indexed citations. Recurring topics across this work include Statistical Methods and Inference (69 papers), Advanced Statistical Methods and Models (32 papers), Bayesian Methods and Mixture Models (19 papers), Statistical Methods and Bayesian Inference (17 papers), Control Systems and Identification (15 papers), Efficiency Analysis Using DEA (15 papers), Economic Growth and Productivity (6 papers) and Economic and Environmental Valuation (5 papers). The work is most often cited by research in Statistics and Probability (1.3k citations), Management Science and Operations Research (984 citations), General Economics, Econometrics and Finance (335 citations), Economics and Econometrics (850 citations) and Finance (260 citations). Byeong U. Park has collaborated with scholars based in South Korea, Belgium and Germany. Frequent co-authors include Léopold Simar, J. S. Marron, Enno Mammen, Peter Hall, J. S. Marron, Aloïs Kneip, Eun Ryung Lee, Richard J. Samworth, Young Lee and Seok‐Oh Jeong. Their work appears in journals such as Journal of the American Statistical Association, The Annals of Statistics, Bernoulli, Journal of Multivariate Analysis and Scandinavian Journal 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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