Byeong U. Park

4.7k total citations
97 papers, 3.0k citations indexed

About

Byeong U. Park is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Byeong U. Park has authored 97 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Statistics and Probability, 26 papers in Artificial Intelligence and 24 papers in Management Science and Operations Research. Recurrent topics in Byeong U. Park's work include Statistical Methods and Inference (69 papers), Advanced Statistical Methods and Models (32 papers) and Bayesian Methods and Mixture Models (19 papers). Byeong U. Park is often cited by papers focused on Statistical Methods and Inference (69 papers), Advanced Statistical Methods and Models (32 papers) and Bayesian Methods and Mixture Models (19 papers). Byeong U. Park collaborates with scholars based in South Korea, Belgium and Germany. Byeong U. Park's 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 and has published in prestigious journals such as Journal of the American Statistical Association, Journal of Econometrics and Journal of the Royal Statistical Society Series B (Statistical Methodology).

In The Last Decade

Byeong U. Park

92 papers receiving 2.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Byeong U. Park South Korea 28 1.3k 984 850 538 335 97 3.0k
Aloïs Kneip Germany 29 879 0.7× 793 0.8× 870 1.0× 340 0.6× 244 0.7× 60 2.8k
Yuhong Yang United States 28 1.5k 1.1× 611 0.6× 387 0.5× 1.0k 1.9× 272 0.8× 124 3.9k
Gerda Claeskens Belgium 30 2.1k 1.6× 504 0.5× 614 0.7× 694 1.3× 265 0.8× 135 3.7k
Daniel Peña Spain 30 1.1k 0.8× 392 0.4× 912 1.1× 596 1.1× 377 1.1× 148 3.4k
Hrishikesh D. Vinod United States 27 661 0.5× 346 0.4× 905 1.1× 352 0.7× 490 1.5× 144 3.1k
Zongwu Cai United States 27 1.7k 1.3× 465 0.5× 1.0k 1.2× 445 0.8× 599 1.8× 102 3.0k
Qiwei Yao United Kingdom 29 1.6k 1.2× 411 0.4× 1.2k 1.4× 561 1.0× 627 1.9× 98 3.7k
Jens Perch Nielsen United Kingdom 30 1.5k 1.1× 900 0.9× 1.0k 1.2× 428 0.8× 213 0.6× 152 3.3k
Rong Chen United States 22 729 0.6× 288 0.3× 569 0.7× 1.1k 2.1× 233 0.7× 76 3.3k
Stefan Sperlich Switzerland 21 873 0.7× 272 0.3× 452 0.5× 384 0.7× 194 0.6× 101 2.2k

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-authorship network of co-authors of Byeong U. Park

This figure shows the co-authorship network connecting the top 25 collaborators of Byeong U. Park. A scholar is included among the top collaborators of Byeong U. Park 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 Byeong U. Park. Byeong U. Park 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
1.
Lee, Eun Ryung, Seyoung Park, Enno Mammen, & Byeong U. Park. (2024). Efficient functional Lasso kernel smoothing for high-dimensional additive regression. The Annals of Statistics. 52(4).
2.
Lee, Young, et al.. (2023). Additive regression with parametric help. Bernoulli. 29(4). 2 indexed citations
3.
Yun, Ho Hyun & Byeong U. Park. (2023). Exponential concentration for geometric-median-of-means in non-positive curvature spaces. Bernoulli. 29(4).
4.
Lee, Young, Enno Mammen, & Byeong U. Park. (2023). Hilbertian additive regression with parametric help. Journal of nonparametric statistics. 35(3). 622–641.
5.
Park, Byeong U., et al.. (2018). Smooth backfitting for errors-in-variables additive models. The Annals of Statistics. 46(5). 14 indexed citations
6.
Park, Byeong U., et al.. (2014). Efficient estimation for partially linear varying coefficient models when coefficient functions have different smoothing variables. Journal of Multivariate Analysis. 126. 100–113. 8 indexed citations
7.
Lee, Young, Enno Mammen, & Byeong U. Park. (2012). Flexible generalized varying coefficient regression models. The Annals of Statistics. 40(3). 40 indexed citations
8.
Lee, Eun Ryung & Byeong U. Park. (2011). Sparse estimation in functional linear regression. Journal of Multivariate Analysis. 105(1). 1–17. 30 indexed citations
9.
Lee, Young, Enno Mammen, & Byeong U. Park. (2010). Backfitting and smooth backfitting for additive quantile models. The Annals of Statistics. 38(5). 38 indexed citations
10.
Park, Byeong U., et al.. (2010). SPARSE VARYING COEFFICIENT MODELS FOR LONGITUDINAL DATA. Statistica Sinica. 20. 1183–1202. 32 indexed citations
11.
Kim, Tae Yoon, et al.. (2009). Using bimodal kernel for inference in nonparametric regression with correlated errors. Journal of Multivariate Analysis. 100(7). 1487–1497. 13 indexed citations
12.
Hall, Peter, et al.. (2007). A Method for Projecting Functional Data Onto a Low-Dimensional Space. Journal of Computational and Graphical Statistics. 16(4). 799–812. 10 indexed citations
13.
Choi, Hyemi, et al.. (2005). Non-parametric hazard function estimation using the Kaplan–Meier estimator. Journal of nonparametric statistics. 17(8). 937–948. 7 indexed citations
14.
Park, Byeong U., et al.. (2003). Adaptive variable location kernel density estimators with good performance at boundaries. Journal of nonparametric statistics. 15(1). 61–75. 7 indexed citations
15.
Eguchi, Shinto, Tae Yoon Kim, & Byeong U. Park. (2003). Local likelihood method: a bridge over parametric and nonparametric regression. Journal of nonparametric statistics. 15(6). 665–683. 13 indexed citations
16.
Hall, Peter & Byeong U. Park. (2003). Bandwidth choice for local polynomial estimation of smooth boundaries. Journal of Multivariate Analysis. 91(2). 240–261. 14 indexed citations
17.
Gijbels, Irène, Enno Mammen, Byeong U. Park, & Léopold Simar. (1999). On Estimation of Monotone and Concave Frontier Functions. Journal of the American Statistical Association. 94(445). 220–228. 139 indexed citations
18.
Hall, Peter, Byeong U. Park, & Steven Stern. (1998). On Polynomial Estimators of Frontiers and Boundaries. Journal of Multivariate Analysis. 66(1). 71–98. 45 indexed citations
19.
Koo, Ja‐Yong & Byeong U. Park. (1996). B-Spline deconvolution based on the Em algorithm. Journal of Statistical Computation and Simulation. 54(4). 275–288. 13 indexed citations
20.
Park, Byeong U.. (1993). A Cross-Validatory Choice of Smoothing Parameter in Adaptive Location Estimation. Journal of the American Statistical Association. 88(423). 848–854. 5 indexed citations

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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