Activity day:2022-09-30
Published At:2022-09-15
Views:503
2022-09-15 updated
【 9 月 30 日 CRETA Seminar】
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.