Quantile Regression for Spatial Data

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.

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 Includes supplementary material: sn.pub/extras

Autorentext
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.

Inhalt
1 Quantile Regression: An Overview. 2 Linear and Nonparametric Quantile Regression.- 3 A Quantile Regression Analysis of Assessment Regressivity.-4 Quantile Version of the Spatial AR Model.- 5 . Conditionally Parametric Quantile Regression.- 6 Guide to Further Reading.- References.