The Data Science Convergence Research Center at Chung-Ang University will hold a seminar on March 20, 2026.

Speaker: Professor Joon-Pyo Kim, Department of Applied Statistics, Chung-Ang University

Date: March 20, 2026, 16:00–17:00

Location: Room 413, Building 310

Joon-Pyo Kim, Department of Applied Statistics, Chung-Ang University

Quantile and quantile-like inference for spatio-temporal data

This talk presents recent developments in quantile and expectile inference for spatio-temporal data. A regression quantile is traditionally defined as the minimizer of the sum of check loss residuals. From this perspective, a number of studies have extended the quantile regression framework by incorporating alternative asymmetric loss functions, such as asymmetric L2 and Huber loss functions. This presentation introduces several statistical methodologies and case studies within this field, including: (1) extreme value analysis for climatological data, (2) expectile inference on river network data, and (3) nonparametric change point analysis.