Quantile Regression Spatial Data by Mcmillen Daniel (16 results)
Language: English
Published by Springer 2012
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Paperback or Softback. Condition: New. Quantile Regression for Spatial Data. Book.
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Published by Springer 2012-08 2012
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Condition: New. pp. 78.
Language: English
Published by Springer 2012
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Paperback. Condition: Brand New. 2013 edition. 75 pages. 9.20x6.40x1.00 inches. In Stock.
Language: English
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg 2012
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory vari…able, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail.
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Taschenbuch. Condition: Neu. Quantile Regression for Spatial Data | Daniel P. Mcmillen | Taschenbuch | SpringerBriefs in Regional Science | ix | Englisch | 2012 | Springer | EAN 9783642318146 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com |… Anbieter: preigu.
Language: English
Published by Springer Berlin Heidelberg Aug 2012 2012
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an…explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail. 76 pp. Englisch.
Language: English
Published by Springer 2012
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Condition: New. Print on Demand pp. 78 47 Illus.
Language: English
Published by Springer 2012
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Condition: New. PRINT ON DEMAND pp. 78.
Language: English
Published by Springer Berlin Heidelberg 2012
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Emphasis on graphical interpretation of quantile regression resultsPresents estimators designed specifically for the analysis of spatial data Includes both parametric and nonparametric approaches Includes both paramet…ric and nonparam.
Language: English
Published by Springer, Springer Aug 2012 2012
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an expl…anatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Both parametric and nonparametric versions of spatial models are considered in detail.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 76 pp. Englisch.
