SpringerBriefs in Regional Science

Quantile Regression for Spatial Data

Authors: McMillen, Daniel P.

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  • Emphasis on graphical interpretation of quantile regression results
  • Presents estimators designed specifically for the analysis of spatial data
  • Includes both parametric and nonparametric approaches
  • Includes both parametric and nonparametric
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  • ISBN 978-3-642-31815-3
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About this book

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.

About the authors

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.

Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook $54.99
price for USA in USD
  • ISBN 978-3-642-31815-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • Immediate eBook download after purchase and usable on all devices
  • Bulk discounts available
Softcover $69.99
price for USA in USD
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Bibliographic Information

Bibliographic Information
Book Title
Quantile Regression for Spatial Data
Authors
Series Title
SpringerBriefs in Regional Science
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
The Author(s)
eBook ISBN
978-3-642-31815-3
DOI
10.1007/978-3-642-31815-3
Softcover ISBN
978-3-642-31814-6
Series ISSN
2192-0427
Edition Number
1
Number of Pages
IX, 66
Number of Illustrations
47 b/w illustrations
Topics