Spatially Varying Coefficient Regression Models

The realization in the statistical and geographical
sciences that relationships between explanatory
variables and a response variable in a regression
model may vary across a study area has lead to the
development of regression models with spatially
varying coefficients. Two such models are
geographically weighted regression and Bayesian
regression models with spatially varying
coefficients. In the application of these models,
inference on the regression coefficient spatial
processes is typically of primary interest. The
presence of collinearity necessitates the use of
diagnostic tools in local regression model building
to highlight areas in which the results are not
reliable for statistical inference, in addition to
presenting an opportunity for remedial methods. This
book, therefore, provides diagnostic tools and
remedial methods for spatially varying coefficient
regression models and includes real-world and
simulated examples demonstrating the utility of the
new techniques. This book sheds light on the issues
of implementing and interpreting results from these
models and should prove especially useful to spatial
data analysts in Geography, Statistics, and Public
Health.

Auteur
David C Wheeler, PhD, MAS, MA: Studied Statistics and Geography at The Ohio State University. Pursuing Master's in Public Health at Harvard University. Cancer Prevention Fellow at the National Cancer Institute, Rockville, MD.

Texte du rabat
The realization in the statistical and geographical sciences that relationships between explanatory variables and a response variable in a regression model may vary across a study area has lead to the development of regression models with spatially varying coefficients. Two such models are geographically weighted regression and Bayesian regression models with spatially varying coefficients. In the application of these models, inference on the regression coefficient spatial processes is typically of primary interest. The presence of collinearity necessitates the use of diagnostic tools in local regression model building to highlight areas in which the results are not reliable for statistical inference, in addition to presenting an opportunity for remedial methods. This book, therefore, provides diagnostic tools and remedial methods for spatially varying coefficient regression models and includes real-world and simulated examples demonstrating the utility of the new techniques. This book sheds light on the issues of implementing and interpreting results from these models and should prove especially useful to spatial data analysts in Geography, Statistics, and Public Health.