| Description |
1 online resource (xix, 463 pages) : illustrations |
| Series |
Chapman & Hall/CRC handbooks of modern statistical methods |
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Chapman & Hall/CRC handbooks of modern statistical methods.
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| Contents |
Introduction -- 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 |
| Summary |
"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."--Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from electronic title page (ProQuest Ebook Central, viewed January 9, 2018) |
| Subject |
Quantile regression.
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Regression analysis.
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Regression Analysis
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Quantile regression
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Regression analysis
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| Genre/Form |
Handbooks and manuals.
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| Form |
Electronic book
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| Author |
Koenker, Roger, 1947- editor.
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Chernozhukov, Victor, editor.
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He, Xuming, editor.
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Peng, Limin, editor.
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| ISBN |
9781315120256 |
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1315120259 |
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9781351646567 |
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1498725287 |
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9781498725286 |
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1351646567 |
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