Quantile Regression for Spatial Data (SpringerBriefs in Regional Science)

Daniel P. McMillen

ISBN 10: 3642318142 / ISBN 13: 9783642318146

Published by Springer 2012-08-01, 2012

About this Item

Seller Inventory # 6666-IUK-9783642318146

Bibliographic Details

• Title: Quantile Regression for Spatial Data (SpringerBriefs in Regional Science)
• Publisher: Springer 2012-08-01
• Publication Date: 2012
• Language: English
• ISBN 10: 3642318142
• ISBN 13: 9783642318146
• Binding: Paperback
• Condition: New

About this title

Synopsis

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 Author

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.