Handbook of Quantile Regression
Language: English
Published by CRC Press, 2020
Series: Book 15 of 27 - Chapman & Hall/CRC Handbooks of Modern Statistical Methods
- Softcover
- New
Condition: New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Roger Koenker, University of IllinoisVictor Chernozhukov, MITXuming He, University of Michigan Limin Peng, Emory UniversityQuantile regression constitutes an ensemble of statistica.
Seller Inventory # 395500827
- Title
- Handbook of Quantile Regression
- Publisher
- CRC Press
- Publication year
- 2020
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0367657570
- ISBN 13
- 9780367657574
- Series
- Book 15 of 27: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
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.
"Synopsis" may belong to another edition of this title.
About the Author
Roger Koenker, University of Illinois
Victor Chernozhukov, MIT
Xuming He, University of Michigan
Limin Peng, Emory University
"About the title" may belong to another edition of this title.
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