Handbook of Quantile Regression

Roger Koenker

ISBN 10: 1498725287 / ISBN 13: 9781498725286

Published by Taylor & Francis Ltd (Sales) Okt 2017, 2017

About this Item

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.

Seller Inventory # 9781498725286

Bibliographic Details

• Title: Handbook of Quantile Regression
• Publisher: Taylor & Francis Ltd (Sales) Okt 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 1498725287
• ISBN 13: 9781498725286
• Binding: Buch
• Condition: Neu
• Dimensions: 264W x 182H x 32D millimeters 264 millimeters width by 182 millimeters height by 32 millimeters depth
• Weight: 1038 grams

About this title

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.

About the Author

Roger Koenker, University of Illinois

Victor Chernozhukov, MIT

Xuming He, University of Michigan

Limin Peng, Emory University