Assessing model adequacy in possibly misspecified quantile regression

DOI10.1016/J.CSDA.2012.07.020zbMath1365.62143OpenAlexW2028745284MaRDI QIDQ2359512

Hohsuk Noh, Anouar El Ghouch, Ingrid Van Keilegom

Publication date: 29 June 2017

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.07.020

zbMATH Keywords

linear modelcoefficient of determinationconditional quantileslack-of-fitprediction quality

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Order statistics; empirical distribution functions (62G30)

Related Items (3)

Assessing quantile prediction with censored quantile regression models ⋮ A lack-of-fit test for quantile regression models with high-dimensional covariates ⋮ On the predictive risk in misspecified quantile regression

Cites Work

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• Coefficients of determination for least absolute deviation analysis
• Analysis of least absolute deviation
• Regression Quantiles
• Some Methodological Aspects of Validation of Models in Nonparametric Regression
• Goodness of Fit and Related Inference Processes for Quantile Regression
• Quality of Fit Measures in the Framework of Quantile Regression
• Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure

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