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Quantile Regression
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  • Cited by 1308
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Book description

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.

Reviews

'… well written and easy to read, with useful illustrations of important aspects of quantile regression. It is obvious that the author knows the subject inside out, giving an up-to-date, exhaustive account of the subject. … The book is a valuable contribution to the statistical literature, and a must have for every statistician or econometrician interested in quantile regression methods.'

Source: Journal of the Royal Statistical Society

'It is well written and easy to read, with useful ilustrations of important aspects of quantile regression … a valuable contribution to the statistical literature and is essential for every statistician or econometrician who is interested in quantile regression methods.'

Andreas Karlsson - Uppsala University

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