Nicolas Christou

English

Research Papers in Economics

JSS Journal of Statistical Software
March 2005, Volume 12, Book Review 3. http://www.jstatsoft.org/
Reviewer: Nicolas Christou
University of California at Los Angeles
Spatial Statistics and Computational Methods
Jesper Moller
Springer, New York, NY, 2003.
ISBN 0-387-00136-0. 202 pp. $67.95 (P).
Chapter 1 provides the reader with a very good overview of the MCMC methodology (Gibbs
sampler and Metropolis-Hastings algorithm) which are used in the other chapters of the text.
It can serve as a reading material for a graduate course that discusses these topics.
Chapter 2 does an excellent job introducing model-based geostatistics. It is deﬁnitely a well-
written chapter and can be used for a graduate course in spatial statistics, but can also be
useful for researchers using spatial statistics. The authors present two examples that help the
reader to understand the methodology used. The maximum likelihood estimation of the Gaus-
sian spatial model is preferred over ordinary least squares ﬁtting of a parametric variogram to
the sample variogram. The authors mention two disadvantages of this method (computation-
ally expensive, and lack of robustness), but they do not mention that the estimates are biased,
and the bias can be large in small to moderate samples. However, they somehow imply this
when they state that the restricted maximum likelihood estimation (which is also discussed)
produces less biased estimates. In the case study of the Swiss rainfall data, the method
of maximum likelihood is used to estimate the model parameters and use them for spatial
prediction. Perhaps a comparison of these predicted values with those that ordinary kriging
produces (using the ﬁtting of a theoretical to the empirical variogram) would be interesting.
Chapter 3 reviews Bayesian modeling and computing methods for image analysis and pro-
cessing. It is a good tutorial, even though it only covers a small part of image analysis and
computer vision.
Chapter 4 deals with simulation-based inference for spatial processes. This is a very well-
written chapter on the increasingly important topic of point processes. It is well suited for
graduate students and experienced researchers in the ﬁeld. Perhaps better examples could
have been used, instead of the weed plants of a Danish barley ﬁeld. There are many examples
from science and technology that can be analyzed using sophisticated methods of spatial
statistics such as MCMC methods presented in this chapter.2 Spatial Statistics and Computational Methods
Reviewer:
Nicolas Christou
University of California at Los Angeles
Department of Statistics
Los Angeles, CA 90095, United States of America
E-mail: nchristo@stat.ucla.edu
URL: http://www.stat.ucla.edu/~nchristo/
Journal of Statistical Software Published: 2005-03-14
March 2005, Volume 12, Book Review 3.
http://www.jstatsoft.org/

Spatial Statistics and Computational Methods

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Research Papers in Economics