Analysing positive-valued
spatial data: the transformed Gaussian model
The Gaussian
assumption is often inappropriate for analysing some kinds of
geostatistical data and transformations can be used in
an attempt to get nearly-Gaussian behaviour. Here we study the
transformed Gaussian model, which includes an additional parameter
corresponding to the Box-Cox family of transformations.
In particular we consider maximum
likelihood estimation and minimum mean square error
prediction for this model, and as an example we use it to model rainfall
data. We discuss the limitations of the transformed Gaussian model,
and suggest that it should be used primarily
as a first line of attack in dealing
with non-Gaussianity and non-linearity, before proceding to more
complex models.
This talk is based on joint work with Peter Diggle and Paulo Ribeiro Jr.
Last modified: Thu Nov 2 11:22:16 MET 2000