Model Selection and Inference
A Practical Information-Theoretic Approach
Published on 1. October 1998
Book
Hardback
XX, 355 pages
978-0-387-98504-6 (ISBN)
Description
Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.
More details
Edition
1st ed. 1998. Corr. 2nd printing
Language
English
Place of publication
NY
United States
Target group
College/higher education
Professional and scholarly
Researchers, graduate students
Illustrations
51
10 s/w Abbildungen, 51 s/w Tabellen
21 figures
Weight
680 gr
ISBN-13
978-0-387-98504-6 (9780387985046)
DOI
10.1007/978-1-4757-2917-7
Schweitzer Classification
Other editions
New editions
Kenneth P. Burnham | David R. Anderson
Model Selection and Multimodel Inference
A Practical Information-Theoretic Approach
Book
07/2002
2nd Edition
Springer
€235.39
Shipment within 5-7 days
Content
Introduction * Information Theory and Log-Likelihood Models: A Basis for Model Selection and Inference * Practical Use of the Information-Theoretic Approach * Model-Selection Uncertainty with Examples * Monte Carlo and Example-Based Insights * Statistical Theory * Summary