A Lasso-Type Approach for Estimation and Variable Selection in Single Index Models

By Peng Zeng, Tianhong He, Y. Zhu. 2012

51 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3d4d1f4768bb52800149975576612472b76dfc01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weining Wang
2. Wolfgang Karl Härdle
3. Hua Liang
4. Jan Peters
5. Duy Nguyen-Tuong

In Statistics and Probability

1. Weining Wang
2. Yan Fan
3. Lixing Zhu
4. Wolfgang Karl Härdle
5. Vladimir Spokoiny
6. Hua Liang
7. Rangan Gupta
8. M. Balcılar

Citations in the corpus

• Single-Index-Based CoVaR With Very High-Dimensional Covariates (2018, background): “Kong and Xia (2007), Wang and Yin (2008), and Zeng, He, and Zhu (2012) focused on variable selection in mean regression for the single-index model.”
• Composite Quantile Regression for the Single-Index Model (2013, unknown): “The difficulty of characterizing an entire distribution partly arises from the high dimensionality of covariates, which asks for striking a balance between model flexibility and statistical precision.”