Ja‐Yong Koo

1.5k total citations
94 papers, 1.0k citations indexed

About

Ja‐Yong Koo is a scholar working on Statistics and Probability, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Ja‐Yong Koo has authored 94 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistics and Probability, 18 papers in Computational Mechanics and 17 papers in Artificial Intelligence. Recurrent topics in Ja‐Yong Koo's work include Statistical Methods and Inference (26 papers), Advanced Statistical Methods and Models (16 papers) and Sparse and Compressive Sensing Techniques (11 papers). Ja‐Yong Koo is often cited by papers focused on Statistical Methods and Inference (26 papers), Advanced Statistical Methods and Models (16 papers) and Sparse and Compressive Sensing Techniques (11 papers). Ja‐Yong Koo collaborates with scholars based in South Korea, Canada and United States. Ja‐Yong Koo's co-authors include Peter T. Kim, Jianqing Fan, Sunghwan Kim, Changyi Park, Kjell A. Doksum, Youngnam Han, Hoon Kim, Hanchul Kim, David Pommerenke and Jae Won Lee and has published in prestigious journals such as Physical Review Letters, Nano Letters and Journal of the American Statistical Association.

In The Last Decade

Ja‐Yong Koo

90 papers receiving 971 citations

Peers

Ja‐Yong Koo

EEE

CVPR

Chii-Ruey Hwang Taiwan

Arnaud Guyader France

Tim van Erven Netherlands

Frédéric Cérou France

Jean‐François Bercher France

Matan Gavish Israel

Dan Kalman United States

Gersende Fort France

Amarjit Budhiraja United States

Stephen Joe New Zealand

Chii-Ruey Hwang Taiwan

Ja‐Yong Koo

506 ×1.6

57 ×0.3

EEE

108 ×0.6

CVPR

471 ×2.7

68 ×0.6

Citations per year, relative to Ja‐Yong Koo Ja‐Yong Koo (= 1×) peers Chii-Ruey Hwang

Countries citing papers authored by Ja‐Yong Koo

Since Specialization

Citations

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

Fields of papers citing papers by Ja‐Yong Koo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ja‐Yong Koo. 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 Ja‐Yong Koo. The network helps show where Ja‐Yong Koo may publish in the future.

Co-authorship network of co-authors of Ja‐Yong Koo

This figure shows the co-authorship network connecting the top 25 collaborators of Ja‐Yong Koo. A scholar is included among the top collaborators of Ja‐Yong Koo 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 Ja‐Yong Koo. Ja‐Yong Koo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Koo, Ja‐Yong, et al.. (2025). Dimensionality reduction in multivariate nonparametric regression via nuclear norm penalization. Statistical Papers. 66(3). 1 indexed citations

Kim, Yong‐Ku, et al.. (2025). Low‐Dimensional Adaptive Neural Network Regression With Directional Change Detection via Nuclear Norm Penalization. Statistical Analysis and Data Mining The ASA Data Science Journal. 18(4).

Koo, Ja‐Yong, et al.. (2022). Intrinsic spherical smoothing method based on generalized Bézier curves and sparsity inducing penalization. Journal of Applied Statistics. 50(9). 1942–1961. 1 indexed citations

Koo, Ja‐Yong, et al.. (2022). Minimax estimation in multi-task regression under low-rank structures. Journal of nonparametric statistics. 35(1). 122–144. 2 indexed citations

Koo, Ja‐Yong, et al.. (2021). Additive regression splines with total variation and non negative garrote penalties. Communication in Statistics- Theory and Methods. 51(22). 7713–7736. 3 indexed citations

Koo, Ja‐Yong, et al.. (2019). Spatially adaptive binary classifier using B-splines and total variation penalty. Journal of nonparametric statistics. 31(4). 887–910. 1 indexed citations

Kim, Sunghwan, et al.. (2017). Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information. Computational and Mathematical Methods in Medicine. 2017. 1–10. 3 indexed citations

Kim, Sunghwan, et al.. (2017). Meta-analytic support vector machine for integrating multiple omics data. BioData Mining. 10(1). 2–2. 96 indexed citations

Kim, Sunghwan, et al.. (2017). Joint Modeling for Mean Vector and Covariance Estimation with l1-Penalty. 36(1). 33–38. 1 indexed citations

10.

Koo, Ja‐Yong, et al.. (2016). Penalized B-spline estimator for regression functions using total variation penalty. Journal of Statistical Planning and Inference. 184. 77–93. 13 indexed citations

11.

Ahn, Hyunjung, et al.. (2015). Geodesic Clustering for Covariance Matrices. Communications for Statistical Applications and Methods. 22(4). 321–331. 4 indexed citations

12.

Koo, Ja‐Yong & Peter T. Kim. (2006). Sharp adaptation for spherical inverse problems with applications to medical imaging. Journal of Multivariate Analysis. 99(2). 165–190. 8 indexed citations

13.

Kim, Peter T., et al.. (2003). Sharp minimaxity and spherical deconvolution for super-smooth error distributions. Journal of Multivariate Analysis. 90(2). 384–392. 10 indexed citations

14.

Kim, Peter T. & Ja‐Yong Koo. (2002). Optimal Spherical Deconvolution. Journal of Multivariate Analysis. 80(1). 21–42. 32 indexed citations

15.

Lee, Geunseop, et al.. (2001). Structure of the Ba-InducedSi(111)-(3×2)Reconstruction. Physical Review Letters. 87(5). 56104–56104. 61 indexed citations

16.

Doksum, Kjell A. & Ja‐Yong Koo. (2000). On spline estimators and prediction intervals in nonparametric regression. Computational Statistics & Data Analysis. 35(1). 67–82. 34 indexed citations

17.

Koo, Ja‐Yong. (1998). Convergence Rates for Logspline Tomography. Journal of Multivariate Analysis. 67(2). 367–384. 1 indexed citations

18.

Koo, Ja‐Yong. (1997). Spline Estimation of Discontinuous Regression Functions. Journal of Computational and Graphical Statistics. 6(3). 266–284. 40 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.

Koo, Ja‐Yong & Youngjo Lee. (1994). Bivariate B-splines in generalised linear models. Journal of Statistical Computation and Simulation. 50(1-2). 119–129. 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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