臺灣經濟計量學會

日期：2022 年 9 月 30 日 (週五) 下午 2:00~3:30

地點：線上舉行

講者：陳樂昱教授 (中央研究院經濟研究所)

演講主題：

Sparse Quantile Regression 

講題摘要：

We study the L0-penalized and L0-constrained quantile regression estimators. For both estimators, we derive non-asymptotic upper bounds on the mean excess quantile prediction risk as well as mean-square parameter and regression function estimation errors. Further,we characterize expected Hamming loss for the L0-penalized estimator. We implement the proposed procedure via mixed integer linear programming and also a more scalable first-order approximation algorithm. We illustrate the finite-sample performance of our approach in Monte Carlo experiments and its usefulness in a real data application concerning conformal prediction of infant birth weights. In sum, our L0-based method produces a much sparser estimator than the L1-penalized and non-convex penalized approaches without compromising precision.

陳樂昱教授 (中央研究院經濟研究所)

報名期限：2022/9/29 (四)  中午 12:00

歡迎各位踴躍報名參加！