학술저널
Quantile Regression with Generalized Doubly Regularized framework
- 호서대학교 기초과학연구소
- 기초과학연구 논문집
- 제16권 제1호
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2008.12115 - 123 (9 pages)
- 3
On highly correlated data, it is known that the doubly regularized quantile regression with Lx and L2 performs better than other regularized quantile regression methods. In this paper, we consider the general framework of quantile regression with : doubly regularized penalties Lx and L2. The proposed method includes the doubly regularization, incentive regularization and fused lasso regularization. From simulation analysis, we investigate its performance.
Ⅰ. Introduction
Ⅱ. Methdologies
Ⅲ. Numerical Study
Ⅳ. Discussion
Ⅴ. Reference
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