Kenneth P. Burnham, David R. Anderson

Model Selection and Multimodel Inference (eBook, PDF)

A Practical Information-Theoretic Approach

• Format: PDF

Produktbeschreibung

Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. The book will be of general interest, but the emphasis is on applications to the biological sciences.

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Produktdetails

• Verlag: Springer US
• 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.