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Handbook of Quantile Regression

Edited by: Roger Koenker , Victor Chernozhukov , Xuming He , Limin Peng

Print publication date:  October  2017
Online publication date:  October  2017

Print ISBN: 9781498725286
eBook ISBN: 9781315120256
Adobe ISBN:

10.1201/9781315120256
 Cite  Marc Record

Book description

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.

Table of contents

Prelims Download PDF
Chapter  1:  A Quantile Regression Memoir Download PDF
Chapter  2:  Resampling Methods Download PDF
Chapter  3:  Quantile Regression: Penalized Download PDF
Chapter  4:  Bayesian Quantile Regression Download PDF
Chapter  5:  Computational Methods for Quantile Regression Download PDF
Chapter  6:  Survival Analysis: A Quantile Perspective Download PDF
Chapter  7:  Quantile Regression for Survival Analysis Download PDF
Chapter  8:  Survival Analysis with Competing Risks and Semi-competing Risks Data Download PDF
Chapter  9:  Instrumental Variable Quantile Regression Download PDF
Chapter  10:  Local Quantile Treatment Effects Download PDF
Chapter  11:  Quantile Regression with Measurement Errors and Missing Data Download PDF
Chapter  12:  Multiple-Output Quantile Regression Download PDF
Chapter  13:  Sample Selection in Quantile Regression: A Survey Download PDF
Chapter  14:  Nonparametric Quantile Regression for Banach-Valued Response Download PDF
Chapter  15:  High-Dimensional Quantile Regression Download PDF
Chapter  16:  Nonconvex Penalized Quantile Regression: A Review of Methods, Theory and Algorithms Download PDF
Chapter  17:  QAR and Quantile Time Series Analysis Download PDF
Chapter  18:  Extremal Quantile Regression Download PDF
Chapter  19:  Quantile Regression Methods for Longitudinal Data Download PDF
Chapter  20:  Quantile Regression Applications in Finance Download PDF
Chapter  21:  Quantile Regression for Genetic and Genomic Applications Download PDF
Chapter  22:  Quantile Regression Applications in Ecology and the Environmental Sciences Download PDF
Index Download PDF
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