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Handbook of quantile regression / edited by Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Chapman & Hall / CRC Handbooks of Modern Statistical MethodsPublisher: Boca Raton, FL : CRC Press, 2017Edition: First editionDescription: 1 Online-RessourceISBN:
  • 9781498725293
  • 9781351646567
Subject(s): Additional physical formats: 9781498725286. | Erscheint auch als: Handbook of quantile regression. Druck-Ausgabe. Boca Raton : CRC Press, 2018. xix, 463 SeitenDDC classification:
  • 519.5/36
MSC: MSC: *62-00 | 62G08 | 62N02 | 62M10 | 62P05 | 62P10 | 62P12 | 00B15LOC classification:
  • QA278.2
Online resources: Summary: ""Cover ""; ""Half Title ""; ""Title ""; ""Copyrights ""; ""Dedication ""; ""Contents ""; ""Preface ""; ""Contributors ""; ""Part I. Introduction""; ""Chapter 1. A Quantile Regression Memoir""; ""1.1 Long ago""; ""Chapter 2. Resampling Methods""; "" 2.1 Introduction""; ""2.2 Paired bootstrap ""; ""2.3 Residual-based bootstrap ""; ""2.4 Generalized bootstrap ""; ""2.5 Estimating function bootstrapSummary: ""2.6 Markov chain marginal bootstrap """"2.7 Resampling methods for clustered data ""; ""2.8 Resampling methods for censored quantile regression ""; ""2.9 Bootstrap for post-model selection inference ""; ""Chapter 3. Quantile Regression: Penalized""; ""3.1 Penalized: how""; ""3.1.1 A probability pathSummary: ""3.2.5 Total-variation splines """"3.3 Penalized: what else ""; ""3.3.1 Tuning ""; ""3.3.2 Multiple covariates ""; ""3.3.3 Additive ts, con dence bandaids, and other phantasmagorias ""; ""Chapter 4. Bayesian Quantile Regression""; ""4.1 Introduction""; ""4.2 Asymmetric Laplace likelihood ""; ""4.3 Empirical likelihoodSummary: ""4.4 Nonparametric and semiparametric likelihoods """"4.4.1 Mixture-type likelihood ""; ""4.4.2 Approximate likelihood via quantile process ""; ""4.5 Discussion ""; ""Chapter 5. Computational Methods for Quantile Regression""; ""5.1 Introduction""; ""5.2 Exterior point methods ""; ""5.3 Interior point methods ""; ""5.4 PreprocessingSummary: "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 publisherPPN: PPN: 1007540907Package identifier: Produktsigel: ZDB-4-NLEBK
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