This paper reviews a number of conceptual issues pertaining to the implementation of an
explicit “spatial” perspective in applied econometrics. It provides an overview of the …

This tutorial presents an overview of multilevel or hierarchical data modelling and its
applications in medicine. A description of the basic model for nested data is given and it is …

Spatial data contain locational information as well as attribute information. It is increasingly
recognized that most data sets are spatial in that the attribute being measured is typically …

Among the many uses of hierarchical modeling, their application to the statistical analysis of
spatial and spatio-temporal data from areas such as epidemiology And environmental …

This book unifies and extends latent variable models, including multilevel or generalized
linear mixed models, longitudinal or panel models, item response or factor models, latent …

An accessible introduction to indirect estimation methods, both traditional and model-based.
Readers will also find the latest methods for measuring the variability of the estimates as …

Spatial epidemiology is the description and analysis of the geographical distribution of
disease. It is more important now than ever, with modern threats such as bio-terrorism …

With new chapters addressing spatial patterning in single variables and spatial relations,
this second edition provides guidance to a wide variety of real-world problems. Focusing on …

In the past decade conditional autoregressive modelling specifications have found
considerable application for the analysis of spatial data. Nearly all of this work is done in the …

Disease mapping involves the analysis of geo-referenced disease incidence data and has
many applications, for example within resource allocation, cluster alarm analysis, and …