Quantile regression: A short story on how and why

By Elisabeth Waldmann. 2018

138 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c5e3ff9093edb434e63287303a8edb717bdd5f30

How researchers see this paper

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

In Control and Systems Engineering

1. Robert A. Rigby
2. Mikis D. Stasinopoulos
3. Fernanda De Bastiani

In Statistics and Probability

1. Mikis D. Stasinopoulos
2. Fernanda De Bastiani
3. Helton Saulo
4. Víctor Leiva
5. Robert A. Rigby
6. Luis Sánchez
7. Alan Dasilva
8. Hanns la Fuente‐Mella

Citations in the corpus

• Log‐symmetric quantile regression models (2020, background): “By modeling the conditional quantile rather than the traditionally employed conditional mean, one can cope with outliers, since they only have an influence on the quantile curves close to them (Waldmann, 2018).”
• GAMLSS: A distributional regression approach (2018, methodology): “An introduction to quantile regression is given by Waldmann (2018).”