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Robust Quantile Regression/Evidence
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.

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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
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketBayesian Quantile Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyOLS Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyQuantile Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Generalized linear modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Multiple linear regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Regressionmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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