"Clark brings emerging statistical approaches alive by putting the
ecology first. Writing from the perspective of a field ecologist
who must confront complex data without suppressing important
detail, Clark describes new methods that are well matched to the
richness of real ecological data. At last we have a text that makes
these tools accessible to ecologists."--Stephen R. Carpenter,
University of Wisconsin, Madison
"Jim Clark has been able to pitch his message just right; one
can see the ecological forest "and" the statistical,
distributional, and computational trees at the same time. By
reading this book, statisticians will gain an appreciation for the
complexity of models in the ecological and environmental sciences,
and ecologists will see the potential for hierarchical statistical
modeling in their research arenas. Clark explains his material
extremely well, but he is also rigorous in his statistical
developments."--Noel Cressie, Ohio State University
"Clark's book is monumental--I don't think there is any other
source that provides this range of sources and methods. He presents
a huge amount of useful material, focusing on the development and
application of Bayesian hierarchical models for the analysis of
ecological and environmental models. It's hard to imagine finding
such a collection of information--the results of extensive
experience with recent ecological, environmental, and statistical
literature--in one place. And I heartily agree with the author's
philosophical stances on simplicity and complexity, statistical
pragmatism, and the need for common sense."--Benjamin Bolker,
University of Florida
"I strongly believe that this is potentially a landmark book
inecology. Its integration of modern statistical methods and
ecological theory and data is fundamentally new. The book will
train ecologists and other quantitative scientists in the 'new
modeling techniques' that are becoming ever more prevalent in their
field. In particular, the book describes how one should deal with
complicated problems in which there is uncertainty in data, model,
and parameters. James Clark does a wonderful job of integrating
modern likelihood-based statistical methods as well as describing
and demonstrating the advantages of the Bayesian
approach."--Christopher K. Wikle, University of Missouri,
Columbia
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