Quantile Regression for Spatial Data (SpringerBriefs in Regional Science) - Tapa blanda

Libro 2 de 10: SpringerBriefs in Regional Science

McMillen, Daniel P.

 
9783642318146: Quantile Regression for Spatial Data (SpringerBriefs in Regional Science)
Tapa blanda
ISBN 10: 3642318142 ISBN 13: 9783642318146
Editorial: Springer, 2012

Sinopsis

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.

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Acerca del autor

Daniel McMillen is a Professor of Economics at the University of Illinois, with a joint appointment in the Institute of Government and Public Affairs. He serves as co-editor of Regional Science and Economics.

De la contraportada

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.

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Editorial
Springer
Año de publicación
2012
Idioma
Inglés
ISBN 10 
3642318142
ISBN 13 
9783642318146
Encuadernación
Tapa blanda
Número de páginas
76
Contacto del fabricante
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