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Quantile Regression for Spatial Data

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Paru le : 2012-08-01

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 f...
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Description du livre

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.

À propos

Auteur
Daniel P. McMillen
Éditeur
Springer
Collection
SpringerBriefs in Regional Science
Parution
2012-08-01
Pages
66 pages
EAN papier
9783642318146

Auteur(s) du livre


Defaut-Illustration Daniel P. McMillen

Caractéristiques détaillées - droits

EAN EPUB
9783642318153
Prix
74,19 €
Nombre pages copiables
0
Nombre pages imprimables
6
Taille du fichier
1516 Ko
Ce livre est accessible aux handicaps informations d'accessibilité

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