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A semiparametric quantile regression rank score test for zero-inflated data. [PDF]
BiometricsWang Z, Ling W, Wang T.
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Determinants of Economic Costs Following Road Traffic Injuries in Canada: A Quantile Regression Forests Machine Learning Approach. [PDF]
Clinicoecon Outcomes ResMomenyan S +6 more
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Corrigendum to "Spatial autoregressive quantile regression modeling of gross regional domestic product data in Java Island" [MethodsX 15 (2025) 103621]. [PDF]
MethodsXRahmi S, Sunusi N, Ilyas N.
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Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and Chair-Stand Test. [PDF]
J Cachexia Sarcopenia MuscleGiordano G +3 more
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2014
A guide to the implementation and interpretation of Quantile Regression models. This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues ...
Davino, Cristina +2 more
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A guide to the implementation and interpretation of Quantile Regression models. This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues ...
Davino, Cristina +2 more
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Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Statistica Sinica, 2020
Summary: The quantile regression method is a valuable complement to the classical mean regression, helping to ensure robust and comprehensive data analyses in a variety of applications. We propose a novel envelope quantile regression (EQR) method that adapts a nascent technique called enveloping to improve the efficiency of the standard quantile ...
Ding, Shanshan +3 more
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Summary: The quantile regression method is a valuable complement to the classical mean regression, helping to ensure robust and comprehensive data analyses in a variety of applications. We propose a novel envelope quantile regression (EQR) method that adapts a nascent technique called enveloping to improve the efficiency of the standard quantile ...
Ding, Shanshan +3 more
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Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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On Extreme Regression Quantiles
Extremes, 1999\textit{R. Koenker} and \textit{G. Basset} [Econometrica 46, 33-50, (1978; Zbl 0373.62038)] introduced regression quantiles to generalize the notion of order statistic from the case of a single sample to the linear regression setting. In linear models regression quantiles estimate the conditional quantile of the response at each value of the ...
Portnoy, Stephen, Jurečková, Jana
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2018
Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming.
Domenico Vistocco, Marilena Furno
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Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming.
Domenico Vistocco, Marilena Furno
openaire +3 more sources