Handbook of Quantile Regression

Roger Koenker

ISBN 10: 1498725287 / ISBN 13: 9781498725286

Edité par Taylor & Francis Ltd (Sales) Okt 2017, 2017

A propos de cet article

Neuware - Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss.

N° de réf. du vendeur 9781498725286

Détails bibliographiques

• Titre: Handbook of Quantile Regression
• Éditeur: Taylor & Francis Ltd (Sales) Okt 2017
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 1498725287
• ISBN à 13 chiffres: 9781498725286
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 264 L x 182 H x 32 P (en millimètres) 264 millimètres de largeur par 182 millimètres de hauteur par 32 millimètres de profondeur
• Poids: 1038 grammes

A propos de ce titre

Synopsis

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss.

Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments.

The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings.

The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

À propos de l?auteur

Roger Koenker, University of Illinois

Victor Chernozhukov, MIT

Xuming He, University of Michigan

Limin Peng, Emory University