no code implementations • 9 Jun 2022 • Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods.
no code implementations • 12 Apr 2022 • Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns.
no code implementations • 20 May 2021 • Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order statistic.
no code implementations • 27 Jan 2021 • Rui Fan, Ji Hyung Lee, Youngki Shin
In this paper we propose the adaptive lasso for predictive quantile regression (ALQR).
no code implementations • 6 Mar 2020 • Ji Hyung Lee, Youngki Shin
We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions.
no code implementations • 7 Oct 2018 • Ji Hyung Lee, Zhentao Shi, Zhan Gao
This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency.