Kyongwon Kim

According to our database, Kyongwon Kim authored at least 12 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

Valid asymptotic inference after sufficient dimension reduction in a single-index framework.

Kyongwon Kim, Jun Song, Jae Keun Yoo

Stat. Comput., February, 2026

On the bias of commercial insurance premiums.

Minji Park, Minjeong Park, Kyongwon Kim, Kyuri Jo, Jae Youn Ahn

J. Comput. Appl. Math., 2026

2025

Multivariate response directional regression: a projective resampling approach.

Ahreum Lee, Kyongwon Kim

J. Big Data, December, 2025

Sparse sufficient dimension reduction for directional regression.

Gayun Kwon, Gijeong Noh, Kyongwon Kim

J. Big Data, December, 2025

Vehicle-Specific Virtual Traffic Control Strategy to Reduce the Start-Up Delay for Autonomous Heavy Trucks.

Yohee Han, Saeideh Esmaeili, Haesung Ahn, Yong Hoon Kim, Kyongwon Kim

IEEE Trans. Intell. Veh., October, 2025

On sufficient dimension reduction methods based on a graphical model with non-concave penalty.

Yujin Park, Kyongwon Kim, Jae Keun Yoo

Stat. Comput., August, 2025

Nonlinear Functional Sufficient Dimension Reduction via Principal Fitted Components.

Minjee Kim, Yujin Park, Kyongwon Kim, Jae Keun Yoo

Stat. Comput., August, 2025

Learning causal graphs via nonlinear sufficient dimension reduction.

Eftychia Solea, Bing Li, Kyongwon Kim

J. Mach. Learn. Res., 2025

2024

On Sufficient Graphical Models.

Bing Li, Kyongwon Kim

J. Mach. Learn. Res., 2024

2023

On a nonlinear extension of the principal fitted component model.

Jun Song, Kyongwon Kim, Jae Keun Yoo

Comput. Stat. Data Anal., June, 2023

2022

On cross-distance selection algorithm for hybrid sufficient dimension reduction.

Yujin Park, Kyongwon Kim, Jae Keun Yoo

Comput. Stat. Data Anal., 2022

On principal graphical models with application to gene network.

Kyongwon Kim

Comput. Stat. Data Anal., 2022