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Quantile regression [electronic resource] / Lingxin Hao, Daniel Q. Naiman.

By: Hao, Lingxin.
Contributor(s): Naiman, Daniel Q.
Material type: TextTextSeries: Quantitative applications in the social sciences: no. 07-149.Publisher: Thousand Oaks, Calif. : Sage Publications, c2007Description: 1 online resource (ix, 126 p.) : ill.ISBN: 9781441628336 (electronic bk.); 1441628339 (electronic bk.); 9781412985550 (ebook); 1412985552 (ebook).Subject(s): Social sciences -- Statistical methods | Regression analysis | Sciences sociales -- M�ethodes statistiques | Analyse de r�egression | MATHEMATICS -- Probability & Statistics -- Regression Analysis | Regressieanalyse | Verdelingen (statistiek) | OngelijkhedenGenre/Form: Electronic books.Additional physical formats: Print version:: Quantile regression.DDC classification: 519.5/36 Online resources: EBSCOhost
Contents:
Introduction -- Quantiles and quantile functions -- Quantile-regression model and estimation -- Quantile-regression inference -- Interpretation of quantile-regression estimates -- Interpretation of monotone-transformed QRM -- Application to income inequality in 1991 and 2001.
Summary: 'Quantile Regression' establishes the seldom recognised link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.
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Includes bibliographical references (p. 121-122) and index.

Introduction -- Quantiles and quantile functions -- Quantile-regression model and estimation -- Quantile-regression inference -- Interpretation of quantile-regression estimates -- Interpretation of monotone-transformed QRM -- Application to income inequality in 1991 and 2001.

Description based on print version record.

'Quantile Regression' establishes the seldom recognised link between inequality studies and quantile regression models. Though separate methodological literatures exist for each subject matter, the authors explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.

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Quantile regression by Hao, Lingxin. ©2007
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