Kenneth P. Burnham, David R. Anderson

Model Selection and Multimodel Inference (eBook, PDF)

A Practical Information-Theoretic Approach

• Format: PDF

Produktbeschreibung

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 488
• Erscheinungstermin: 28. Mai 2007
• Englisch
• ISBN-13: 9780387224565
• Artikelnr.: 44007384

Autorenporträt

Kenneth P. Burnham, Colorado State University, Fort Collins, CO, USA / David R. Anderson, Colorado State University, Fort Collins, CO, USA

Inhaltsangabe

Introduction.- Information and Likelihood Theory and Models: A Basis for Model Selection and Inference.- Basic Use of the Information-Theoretic Approach.- Formal Inference From More Than One Model: Multimodel Inference (MMI).- Monte Carlo Insights and Extended Examples.- Advanced Issues and Deeper Insights.- Statistical Theory and Numerical Results.- Summary.