Koenker R., Chernozhukov V., He X., Peng L. Handbook of Quantile Regression

Boca Raton: CRC Press, 2018. — 485 p.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.A Quantile Regression Memoir
Resampling Methods
Quantile Regression: Penalized
Bayesian Quantile Regression
Computational Methods for Quantile Regression
Survival Analysis: A Quantile Perspective
Quantile Regression for Survival Analysis
Survival Analysis with Competing Risks and Semi-competing Risks Data
Instrumental Variable Quantile Regression
Local Quantile Treatment Effects
Quantile Regression with Measurement Errors and Missing Data
Multiple-Output Quantile Regression
Sample Selection in Quantile Regression: A Survey
Nonparametric Quantile Regression for Banach-valued Response
High-Dimensional Quantile Regression
Nonconvex Penalized Quantile Regression: A Review of Methods, Theory and Algorithms
QAR and Quantile Time Series Analysis
Extremal Quantile Regression
Quantile regression methods for longitudinal data
Quantile Regression Applications in Finance
Quantile regression for Genetic and Genomic Applications
Quantile regression applications in ecology and the environmental sciences