Quantile Regression for Spatial Data

  • Daniel P. McMillen

Part of the SpringerBriefs in Regional Science book series (BRIEFSREGION)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Daniel P. McMillen
    Pages 1-11
  3. Daniel P. McMillen
    Pages 13-27
  4. Daniel P. McMillen
    Pages 37-47
  5. Daniel P. McMillen
    Pages 49-60
  6. Daniel P. McMillen
    Pages 61-63
  7. Back Matter
    Pages 65-66

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.


Locally Weighted Regression Nonparametric Quantile Regression Spatial Econometrics

Authors and affiliations

  • Daniel P. McMillen
    • 1
  1. 1.Department of EconomicsUniversity of Illinois Institute of GovernmentUrbanaUSA

Bibliographic information