Method evidence record
Robust Quantile Regression
Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Robust Quantile Regression
Taxonomic method record · regression-model / statistics
- Koenker, R. (2005). Quantile Regression. Cambridge University Press. · ISBN 978-0521608275
- Machado, J. A. F. (1993). Robust model selection and M-estimation. Econometric Theory, 9(3), 478–493. · DOI 10.1017/S0266466600007775
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.