Adaptive penalized quantile regression for high dimensional data

By Qi Zheng, Colin M. Gallagher, K. B. Kulasekera. 2013

48 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/54e965124658592ed1e9dd0645e49cfd1ead2951

How researchers see this paper

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

In Statistics and Probability

1. Weining Wang
2. Jelena Bradic
3. Mladen Kolar
4. Wolfgang Karl Härdle
5. Lining Yu
6. Vladimir Spokoiny
7. Hua Liang
8. Rangan Gupta

Citations in the corpus

• TENET: Tail-Event driven NETwork risk (2016, methodology): “As a remedy to this problem the adaptive LASSO estimation procedure was proposed (Zou (2006); Zheng et al. (2013)).”
• Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores (2017, background): “In the context of quantile regression, a lot of the early work focused on estimation, prediction and variable selection consistency in low (Li and Zhu, 2008; Zou and Yuan, 2008; Wu and Liu, 2009) and high-dimensional setting (Belloni and Chernozhukov, 2011; Wang et al., 2012; Zheng et al., 2013).”