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Handbook of Quantile Regression
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Table of Contents

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

About the Author

Roger Koenker, University of Illinois Victor Chernozhukov, MIT Xuming He, University of Michigan Limin Peng, Emory University

Reviews

"Quantile regression was introduced in 1757 but not perfected until Koenker and Bassett made it a modern tool for robust analyses in linear models in 1978. This book is testimony to its continuing vitality and growing relevance in the big data era." -Stephen M. Stigler, Ernest DeWitt Burton Distinguished Service Professor of Statistics, University of Chicago "Since its invention by Koenker and Bassett, quantile regression has moved from intriguing statistical curiosity to a central empirical tool in the applied econometrician's toolkit. This volume offers a valuable, accessible, and timely summary of the many major methodological developments that have expanded and enriched our understanding of quantile regression and its many applications. Many of the volume's contributors have been active in promoting the "quantile revolution." Practitioners and methodologists alike should find the essays in this Handbook useful and interesting." -Josh Angrist, MIT Department of Economics

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