Quantile Regression
Quantile Regression
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 <a href= "http://www.econ.uiuc.edu/~roger/research/rq/rq.html"></a><br/>
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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.
- First comprehensive study of quantile regression methods
- Tutorial on associated statistical software in R
- Illustrative applications from a broad variety of disciplines
Reviews & endorsements
"Roger Koenker has a profound knowledge of econometrics, linear and non-linear programming, statistics and computational statistics, and a strong intuition, combined with a sense for practical problems. As a result, this excellent book combines all of these above aspects and covers a broad spectrum, from practical applications to the weak convergence of probability measures through examples on maximum daily temperatures to Choquet capacities...this book should definitely be on every statistician's and econometrician's shelf."
Jana Jureckova, Journal of the American Statistical Association
"The author is one [of] the "fathers" of quantile regression. He has substantially contributed to the theoretical as well as the applied development of the field. The book is well written... It provides useful information for statisticians and econometricians, and it can certainly serve as a reference book."
M. Huskova, Mathematical Reviews
Product details
May 2005Paperback
9780521608275
366 pages
229 × 150 × 25 mm
0.54kg
63 b/w illus. 13 tables 20 exercises
Available
Table of Contents
- Part I. Introduction:
- 1. Means and ends
- 2. The first regression: an historical prelude
- 3. Quantiles, ranks, and optimization
- 4. Preview of quantile regression
- 5. Three examples
- 6. Conclusion
- Part II. Fundamentals of Quantile Regression:
- 7. Quantile treatment effects
- 8. How does quantile regression work?
- 9. Robustness
- 10. Interpreting quantile regression models
- 11. Caution: quantile crossing
- 12. A random coefficient interpretation
- 13. Inequality measures and their decomposition
- 14. Expectiles and other variations
- 15. Interpreting misspecified quantile regressions
- 16. Problems
- Part III. Inference for Quantile Regression:
- 17. The finite sample distribution of regression quantiles
- 18. A heuristic introduction to quantile regression asymptotics
- 19. Wald tests
- 20. Estimation of asymptotic covariance matrices
- 21. Rank based Inference for quantile regression
- 22. Quantile likelihood ratio tests
- 23. Inference on the quantile regression process
- 24. Tests of the location/acale hypothesis
- 25. Resampling methods and the bootstrap
- 26. Monte-Carlo comparison of methods
- 27. Problems
- Part IV. Asymptotic Theory of Quantile Regression:
- 28. Consistency
- 29. Rates of convergence
- 30. Bahadur representation
- 31. Nonlinear quantile regression
- 32. The quantile regression rankscore process
- 33. Quantile regression asymptotics under dependent conditions
- 34. Extremal quantile regression
- 35. The method of quantiles
- 36. Model selection, penalties, and large-p asymptotics
- 37. Asymptotics for inference
- 38. Resampling schemes and the bootstrap
- 39. Asymptotics for the quantile regression process
- 40. Problems
- Part V. L-Statistics and Weighted Quantile Regression:
- 41. L-Statistics for the linear model
- 42. Kernel smoothing for quantile regression
- 43. Weighted quantile regression
- 44 Quantile regression for location-scale models
- 45. Weighted sums of p-functions
- 46. Problems
- Part VI. Computational Aspects of Quantile Regression:
- 47. Introduction to linear programming
- 48. Simplex methods for quantile regression
- 49. Parametric programming for quantile regression
- 50 Interior point methods for canonical LPs
- 51. Preprocessing for quantile regression
- 52. Nonlinear quantile regression
- 53. Inequality constraints
- 54. Weighted sums of p-functions
- 55. Sparsity
- 56. Conclusion
- 57. Problems
- Part VII. Nonparametric Quantile Regression:
- 58. Locally polynomial quantile regression
- 59. Penalty methods for univariate smoothing
- 60. Penalty methods for bivariate Smoothing
- 61. Additive models and the Role of sparsity
- Part VIII. Twilight Zone of Quantile Regression:
- 62. Quantile regression for survival data
- 63. Discrete Response models
- 64. Quantile autoregression
- 65. Copula functions and nonlinear quantile regression
- 66. High breakdown alternatives to quantile regression
- 67. Multivariate quantiles
- 68. Penalty methods for longitudinal data
- 69. Causal effects and structural models
- 70. Choquet utility, risk and pessimistic portfolios
- Part IX. Conclusion: A. Quantile regression in R: a vignette
- A.1. Introduction
- A.2. What is a vignette?
- A.3. Getting started
- A.4. Object orientation
- A.5. Formal Inference
- A.6. More on testing
- A.7. Inference on the quantile regression process
- A.8. Nonlinear quantile regression
- A.9. Nonparametric quantile regression
- A.10. Conclusion
- B. Asymptotic critical values.
