Quantile regression with monotonicity restrictions using P-splines and the L1-norm

By K. Bollaerts, Paul H. C. Eilers, Marc Aerts. 2006

62 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/04ff6a0ca224ce6717920febad2702aa75b3c30c

How researchers see this paper

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

In Computational Theory and Mathematics

1. Monica Pratesi
2. Maria Giovanna Ranalli
3. Nicola Salvati

In Control and Systems Engineering

1. Monica Pratesi
2. Maria Giovanna Ranalli
3. Nicola Salvati
4. María Durbán
5. Thomas Kneib

In Management Science and Operations Research

1. Monica Pratesi
2. Maria Giovanna Ranalli
3. Nicola Salvati
4. Paul H. C. Eilers
5. Linda Schulze Waltrup
6. Brian D. Marx
7. Fabian Sobotka
8. Goeran Kauermann

Citations in the corpus

• Expectile and quantile regression—David and Goliath? (2015, methodology): “Bollaerts et al. (2006) made use of penalized B-splines with an L1 penalty. Recently Reiss and Huang (2012) suggested quantile estimation based on penalized iterative least squares (see also Yuan, 2006).”
• Twenty years of P-splines (2015, background): “An example is a monotonously increasing curve fit (Bollaerts et al., 2006).”
• Nonparametric M-quantile regression using penalised splines (2009, methodology): “[15] for quantile regression with monotonicity constraints on the final smooth functions and for robust M-estimates in Lee and Oh [16].”