Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Fundamentals of Quantile Regression
- 3 Inference for Quantile Regression
- 4 Asymptotic Theory of Quantile Regression
- 5 L-Statistics and Weighted Quantile Regression
- 6 Computational Aspects of Quantile Regression
- 7 Nonparametric Quantile Regression
- 8 Twilight Zone of Quantile Regression
- 9 Conclusion
- A Quantile Regression in R: A Vignette
- B Asymptotic Critical Values
- References
- Name Index
- Subject Index
Preface
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Fundamentals of Quantile Regression
- 3 Inference for Quantile Regression
- 4 Asymptotic Theory of Quantile Regression
- 5 L-Statistics and Weighted Quantile Regression
- 6 Computational Aspects of Quantile Regression
- 7 Nonparametric Quantile Regression
- 8 Twilight Zone of Quantile Regression
- 9 Conclusion
- A Quantile Regression in R: A Vignette
- B Asymptotic Critical Values
- References
- Name Index
- Subject Index
Summary
Francis Galton in a famous passage defending the “charms of statistics” against its many detractors, chided his statistical colleagues
[who] limited their inquiries to Averages, and do not seem to revel in more comprehensive views. Their souls seem as dull to the charm of variety as that of a native of one of our flat English counties, whose retrospect of Switzerland was that, if the mountains could be thrown into its lakes, two nuisances would be got rid of at once
(Natural Inheritance, p. 62).It is the fundamental task of statistics to bring order out of the diversity – at times the apparent chaos – of scientific observation. And this task is often very effectively accomplished by exploring how averages of certain variables depend on the values of other “conditioning” variables. The method of least squares, which pervades statistics, is admirably suited for this purpose. And yet, like Galton, one may question whether the exclusive focus on conditional mean relations among variables ignores some “charm of variety” in matters statistical.
As a resident of one of the flattest American counties, my recollections of Switzerland and its attractive nuisances are quite different from the retrospect described by Galton. Not only the Swiss landscape, but also many of its distinguished statisticians have in recent years made us more aware of the charms and perils of the diversity of observations and the consequences of too blindly limiting our inquiry to averages.
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- Quantile Regression , pp. xiii - xviPublisher: Cambridge University PressPrint publication year: 2005
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