World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
37
Citations
7005
World Ranking
2462
National Ranking
11

Research.com Recognitions

  • 2005 - Fellow of the American Statistical Association (ASA)

Overview

Byeong U. Park is affiliated with Seoul National University in South Korea. Their research primarily centers on advanced statistical methods and mathematical approaches within the field of mathematics.

Park's work spans several main and subfields, including:

  • Mathematics
  • Statistics and Probability
  • Artificial Intelligence
  • Applied Mathematics
  • Control and Systems Engineering
  • Mathematical Physics

The scientist's research topics cover a range of areas such as:

  • Statistical Methods and Inference
  • Advanced Statistical Methods and Models
  • Bayesian Methods and Mixture Models
  • Control Systems and Identification
  • Point processes and geometric inequalities
  • Morphological variations and asymmetry
  • Topological and Geometric Data Analysis

Byeong U. Park has published frequently in a number of academic venues, including:

  • Bernoulli
  • The Annals of Statistics
  • Electronic Journal of Statistics
  • Statistics and Its Interface
  • arXiv (Cornell University)

Recent published papers authored or coauthored by Park include:

  • "Additive regression with Hilbertian responses" (2020, The Annals of Statistics)
  • "Additive regression for non-Euclidean responses and predictors" (2021, The Annals of Statistics)
  • "Nonparametric regression with parametric help" (2020, Electronic Journal of Statistics)
  • "Nonparametric regression on Lie groups with measurement errors" (2022, The Annals of Statistics)
  • "Locally polynomial Hilbertian additive regression" (2022, Bernoulli)

Their frequent coauthors include:

  • Young Lee
  • Jeong Min Jeon
  • Enno Mammen
  • Ingrid Van Keilegom
  • Hyerim Hong

In 2005, Byeong U. Park was named a Fellow of the American Statistical Association (ASA).

Best Publications

  • Comparison of Data-Driven Bandwidth Selectors

    Byeong U. Park;J. S. Marron

  • A note on the convergence of nonparametric DEA estimators for production efficiency scores

    Alois Kneip;Byeong U. Park;Léopold Simar

  • Choice of neighbor order in nearest-neighbor classification

    Peter Hall;Byeong U. Park;Richard J. Samworth

  • Smoothed cross-validation

    Peter Hall;Peter Hall;J. S. Marron;J. S. Marron;Byeong U. Park;Byeong U. Park

  • Nonparametric stochastic frontiers: a local maximum likelihood approach

    Subal C. Kumbhakar;Byeong U. Park;Léopold Simar;Efthymios G. Tsionas

  • On estimation of monotone and concave frontier functions

    Irène Gijbels;Enno Mammen;Byeong U. Park;Léopold Simar

  • The FDH estimator for productivity efficiency scores

    Byeong U. Park;Léopold Simar

  • Model Selection via Bayesian Information Criterion for Quantile Regression Models

    Eun Ryung Lee;Hohsuk Noh;Byeong U. Park

  • A simple root n bandwidth selector

    M. C. Jones;J. S. Marron;B. U. Park

  • Practical performance of several data driven bandwidth selectors

    Byeong U. Park;Berwin A. Turlach

  • Versions of Kernel-Type Regression Estimators

    M. C. Jones;S. J. Davies;B. U. Park

  • Efficient Semiparametric Estimation in a Stochastic Frontier Model

    Byeong u. Park;Léopold Simar

  • Nonparametric conditional efficiency measures: asymptotic properties

    Seok-Oh Jeong;Byeong U. Park;Léopold Simar

  • Stochastic panel frontiers: A semiparametric approach☆

    B. U. Park;R. C. Sickles;Léopold Simar

  • Varying Coefficient Regression Models: A Review and New Developments

    Byeong U. Park;Enno Mammen;Young K. Lee;Eun Ryung Lee

  • Time Series Modelling with Semiparametric Factor Dynamics

    Byeong U. Park;Enno Mammen;Wolfgang Härdle;Szymon Borak

  • Smooth backfitting in generalized additive models

    Kyusang Yu;Byeong U. Park;Enno Mammen

  • Estimation of non-sharp support boundaries

    W. Härdle;B. U. Park;A. B. Tsybakov

  • Semiparametric efficient estimation of dynamic panel data models

    Byeong U. Park;Robin C. Sickles;Léopold Simar

  • Semiparametric efficient estimation of AR(1) panel data models.

    Byeong U. Park;Robin C. Sickles;Léopold Simar

  • A Note on the Convergence of Nonparametric DEA Efficiency Measures

    Alois Kneip;Byeong U. Park;Leopold Simar

Frequent Co-Authors

Léopold Simar
Léopold Simar Université Catholique de Louvain
Enno Mammen
Enno Mammen Heidelberg University
James Stephen Marron
James Stephen Marron University of North Carolina at Chapel Hill
Robin C. Sickles
Robin C. Sickles Rice University
M. C. Jones
M. C. Jones The Open University
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Hans-Georg Müller
Hans-Georg Müller University of California, Davis
Alois Kneip
Alois Kneip University of Bonn
Subal C. Kumbhakar
Subal C. Kumbhakar Binghamton University
Piotr Kokoszka
Piotr Kokoszka Colorado State University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, exploring related online degrees can open diverse career pathways. Many opt for business-oriented programs that complement their analytical skills, such as an MBA. Those seeking convenient options often consider the easiest mba online programs, which provide a balanced workload and flexible scheduling.

Financially conscious learners might prioritize affordability without sacrificing quality. For example, the cheapest aacsb online dba degrees offer accredited paths that maintain high standards at lower costs. Similarly, there are options for those interested in finance, with the cheap masters in finance programs providing an economical gateway to careers in financial analysis or consultancy.

Additionally, accelerated learning is appealing to working professionals and ambitious students. The fastest online mba programs enable quicker completion, allowing graduates to enter the job market sooner or advance their careers rapidly.

Choosing the right online degree involves balancing cost, duration, and workload while aligning with career goals. Mathematics students should consider these online programs to enhance their expertise and open doors to various fields like business administration, finance, and data analytics.

Best Scientists Citing Byeong U. Park

Trending Scientists

Recently Published Articles