Quantile Regression

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Cambridge University Press, May 9, 2005 - Business & Economics - 349 pages
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Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. Roger Koenker has devoted more than 25 years of research to the topic. The methods in his analysis are illustrated with a variety of applications from economics, biology, ecology and finance and will target audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above. Author resource page: http://www.econ.uiuc.edu/~roger/research/rq/rq.html

Roger Koenker is the winner of the 2010 Emanuel and Carol Parzen Prize for Statistical Innovation, awarded by the the Department of Statistics at Texas A&M University.
 

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Contents

CHAPTER 1 Introduction
1
CHAPTER 2 Fundamentals of Quantile Regression
26
CHAPTER 3 Inference for Quantile Regression
68
CHAPTER 4 Asymptotic Theory of Quantile Regression
116
CHAPTER 5 LStatistics and Weighted Quantile Regression
151
CHAPTER 6 Computational Aspects of Quantile Regression
173
CHAPTER 7 Nonparametric Quantile Regression
222
CHAPTER 8 Twilight Zone of Quantile Regression
250
CHAPTER 9 Conclusion
293
A Vignette
295
APPENDIX B Asymptotic Critical Values
317
References
319
Name Index
337
Subject Index
342
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About the author (2005)

Roger Koenker is McKinley Professor of Economics and Professor of Statistics at the University of Illinois at Urbana-Champaign. From 1976 to 1983 he was a member of the technical staff at Bell Laboratories. He has held visiting positions at The University of Pennsylvania, Charles University, Prague, Nuffield College, Oxford, University College London and Australian National University. He is a Fellow of the Econometric Society.