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Quantile regression /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5/36
書名/作者:	Quantile regression // Roger Koenker.
作者:	Koenker, Roger,
面頁冊數:	1 online resource (xv, 349 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Regression analysis.
標題:	Mathematical statistics.
ISBN:	9780511754098 (ebook)
內容註:	1. Introduction -- 2. Student Course Evaluations and Class Size -- 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.
摘要、提要註:	Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
電子資源:	http://dx.doi.org/10.1017/CBO9780511754098

Quantile regression /
Koenker, Roger,1947-

Quantile regression /Roger Koenker. - 1 online resource (xv, 349 pages) :digital, PDF file(s). - Econometric Society monographs ;38. - Econometric Society monographs ;36..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

1. Introduction -- 2. Student Course Evaluations and Class Size -- 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.

Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.

ISBN: 9780511754098 (ebook)Subjects--Topical Terms:

186625
Regression analysis.

LC Class. No.: QA278.2 / .K64 2005

Dewey Class. No.: 519.5/36

Quantile regression /
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520 $a Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
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