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The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.
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Autorenporträt
Roger Koenker, University of Illinois Victor Chernozhukov, MIT Xuming He, University of Michigan Limin Peng, Emory University
Inhaltsangabe
A Quantile Regression Memoir - Gilbert W. Bassett Jr. and Roger Koenker Resampling Methods - Xuming He Quantile Regression: Penalized - Ivan Mizera Bayesian Quantile Regression - Huixia Judy Wang and Yunwen Yang Computational Methods for Quantile Regression - Roger Koenker Survival Analysis: A Quantile Perspective - Zhiliang Ying and Tony Sit Quantile Regression for Survival Analysis - Limin Peng Survival Analysis with Competing Risks and Semi-competing Risks Data - Ruosha Li and Limin Peng Instrumental Variable Quantile Regression - Victor Chernozhukov, Christian Hansen, and Kaspar Wuethrich Local Quantile Treatment Effects - Blaise Melly and Kaspar Wuethrich Quantile Regression with Measurement Errors and Missing Data - Ying Wei Multiple-Output Quantile Regression - Marc Hallin and Miroslav Siman Sample Selection in Quantile Regression: A Survey - Manuel Arellano and Stephane Bonhomme Nonparametric Quantile Regression for Banach-valued Response - Joydeep Chowdhury and Probal Chaudhuri High-Dimensional Quantile Regression - Alexandre Belloni, Victor Chernozhukov, and Kengo Kato Nonconvex Penalized Quantile Regression: A Review of Methods, Theory and Algorithms - Lan Wang QAR and Quantile Time Series Analysis - Zhijie Xiao Extremal Quantile Regression -Victor Chernozhukov, Ivan Fernandez-Val, and Tetsuya Kaji Quantile regression methods for longitudinal data - Antonio F. Galvao and Kengo Kato Quantile Regression Applications in Finance - Oliver Linton and Zhijie Xiao Quantile regression for Genetic and Genomic Applications - Laurent Briollais and Gilles Durrieu Quantile regression applications in ecology and the environmental sciences - Brian S. Cade
A Quantile Regression Memoir - Gilbert W. Bassett Jr. and Roger Koenker Resampling Methods - Xuming He Quantile Regression: Penalized - Ivan Mizera Bayesian Quantile Regression - Huixia Judy Wang and Yunwen Yang Computational Methods for Quantile Regression - Roger Koenker Survival Analysis: A Quantile Perspective - Zhiliang Ying and Tony Sit Quantile Regression for Survival Analysis - Limin Peng Survival Analysis with Competing Risks and Semi-competing Risks Data - Ruosha Li and Limin Peng Instrumental Variable Quantile Regression - Victor Chernozhukov, Christian Hansen, and Kaspar Wuethrich Local Quantile Treatment Effects - Blaise Melly and Kaspar Wuethrich Quantile Regression with Measurement Errors and Missing Data - Ying Wei Multiple-Output Quantile Regression - Marc Hallin and Miroslav Siman Sample Selection in Quantile Regression: A Survey - Manuel Arellano and Stephane Bonhomme Nonparametric Quantile Regression for Banach-valued Response - Joydeep Chowdhury and Probal Chaudhuri High-Dimensional Quantile Regression - Alexandre Belloni, Victor Chernozhukov, and Kengo Kato Nonconvex Penalized Quantile Regression: A Review of Methods, Theory and Algorithms - Lan Wang QAR and Quantile Time Series Analysis - Zhijie Xiao Extremal Quantile Regression -Victor Chernozhukov, Ivan Fernandez-Val, and Tetsuya Kaji Quantile regression methods for longitudinal data - Antonio F. Galvao and Kengo Kato Quantile Regression Applications in Finance - Oliver Linton and Zhijie Xiao Quantile regression for Genetic and Genomic Applications - Laurent Briollais and Gilles Durrieu Quantile regression applications in ecology and the environmental sciences - Brian S. Cade
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