- Series: Econometric Society Monographs (Book 38)
- Paperback: 366 pages
- Publisher: Cambridge University Press (May 9, 2005)
- Language: English
- ISBN-10: 0521608279
- ISBN-13: 978-0521608275
- Product Dimensions: 6 x 1 x 9 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #526,907 in Books (See Top 100 in Books)
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Quantile Regression (Econometric Society Monographs)
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"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
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. 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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Quantile regression is based upon a reinterpretation of the least squares method, providing a flexible means of doing data analysis. Rather than seeing a dataset as a "signal contaminated with noise", it suggests all the effects recorded there are worthy of study, if only to understand processes which interfere with primary measurements.
In cases of complicated observations and datasets, it promises to serve as a powerful tool.
Prior to purchase, I recommend Koenker, Hallock, J.Econ.Perspectives 15(4), 143-156, Fall 2001, and especially Cade, Noon, Frontiers Ecol. Environ 2003, 1(8), 412-420 as previews.
I look forward to indulging myself with the text.
However, I bought the Kindle edition of the book, and there are numerous severe copyediting/typographic problems in the book. For instance, section 4.6.1 has an important sentence that never finishes. Somewhere else in the book the maths get jammed. I have not checked the print edition of the book, but these are severe problems that should have been eliminated with copyediting.
Otherwise, this book is crucial for anybody doing quantile regression analysis.
Make a stand against mathematical opacity! Here's another example of the genre: The EM Algorithm and Extensions