Nonparametric Quantile Regression

By Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alex Smola. 2005

74 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0221595b0d1961b5d6177d2c750385228e76e19b

How researchers see this paper

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

1. Pannagadatta K. Shivaswamy
2. Monica Pratesi
3. C. Bhattacharyya
4. Alex Smola
5. Maria Giovanna Ranalli
6. Nicola Salvati
7. Francis R. Bach
8. Kenji Fukumizu

Citations in the corpus

• Second Order Cone Programming Approaches for Handling Missing and Uncertain Data (2006, methodology): “For c(e) = 1 2 e 2 we obtain Gaussian Process regression estimators (Williams, 1998), in the second case we obtain nonparametric median estimates (Le et al., 2005), and finally c(e) = max(0, |e|− ε) yields ε-insensitive SV regression (Vapnik et al.”
• Nonparametric M-quantile regression using penalised splines (2009, methodology): “[8] use a constrained version of smoothing splines for quantile regression to avoid the occurrence of crossing while He [1] proposes a simple way to a posteriori restrict quantile regression while maintaining sufficient modelling flexibility.”