Handbook of Quantile Regression (eBook, ePUB)

Redaktion: Koenker, Roger; Peng, Limin; He, Xuming; Chernozhukov, Victor

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 483
• Erscheinungstermin: 12. Oktober 2017
• Englisch
• ISBN-13: 9781351646567
• Artikelnr.: 49991275

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