Model Based Inference in the Life Sciences: A Primer on Evidence (Paperback)David R. Anderson (author)
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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
Publisher: Springer-Verlag New York Inc.
Number of pages: 184
Weight: 670 g
Dimensions: 235 x 155 x 11 mm
Edition: 1st ed. 2008
From the reviews:
".... The writing style is pragmatic and appropriate for someone without advanced statistical training. Readers looking to recommend a book on information-criteria-based modeling to colleagues who are not statisticians, or looking to locate such a book for their libraries are likely to be satisfied with this book. " (Biometrics, December 2008, Brief Reports by the Editor)
"This ... book provides an introduction to this approach of evidence-based inference. It is focused on advocating and teaching the approach. It includes some history and philosophy with the methods, and each chapter ends with exercises. ... For those who are already familiar with model-based inference ... it provides a more in-depth account of the information theoretical approach. For those who are new to model-based inference, it provides a good conceptual and technical introduction." (Glenn Suter, Integrated Environmental Assessment and Management, Vol. 5 (2), 2009)
"Readership: Researchers and graduate students in ecology and other life sciences. This monograph expounds ideas that the author has developed over many years with Burnham. It is heavily example-based, and aimed at working scientists. Examples are predominately from ecological studies. ... This is an interesting and challenging ... book." (John H. Maindonald, International Statistical Review, Vol. 77 (3), 2009)"...Presents an information-theoretic approach to statistical inference...Well motivated, clearly written, and thought provoking for its targeted readership. ..." (The American Statistician, February 2010, Vol. 64, No. 1)
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