Model Selection and Inference: A Practical Information-Theoretic Approach

Kenneth P. Burnham

ISBN 10: 0387985042 / ISBN 13: 9780387985046

Editore: Springer Verlag, 1998

Riguardo questo articolo

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Codice articolo 00091758189

Dati bibliografici

• Titolo: Model Selection and Inference: A Practical Information-Theoretic Approach
• Editore: Springer Verlag
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 0387985042
• ISBN 13: 9780387985046
• Rilegatura: Rilegato
• Condizione: Very Good

Descrizione articolo

Riassunto

This work covers the philosophy of model-based data analysis and provides an omnibus strategy for the analysis of empirical data. It introduces information theoretical approaches and focuses critical attention on a priori modelling and the selection of a good approximating model that best represents the inference supported by the data. Kullback-Leibler information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. The information theoretic approaches seek to provide a unified theory, an extension of likelihood theory. The work brings model selection and parameter estimation under a common framework - optimization. The value of AIC is computed for each a priori model to be considered and the model with the minimum AIC is used for statistical inference. However, the paradigm described in the book goes beyond the computation and interpretation of AIC to select a parsimonious model for inference from empirical data; it refocuses increased attention on a variety of considerations and modelling prior to the actual analysis of data.

Recensione

From the reviews of the second edition:

Burnham and Anderson (eschew) P-values completely and (focus) entirely on how to decide when a model or models adequately fits the data. In essence, this is what an ecologist wants to know-how do predictive models work? This simple categorization, however, belies the conceptual richness that Burnham and Anderson present in their book, and its importance." (Ecology)

"Bolstered by a new chapter and an additional 140 pages, this very specialized book is now quite a sizable affair in its second edition ... . Subtitled ‘A Practical Information-Theoretic Approach,’ the book is built on the use of the Kullback-Leibler distance approach for multimodel inference. ... The enthusiasm of the authors for their subject is apparent from the effort that they have made to extensively revise what already was a very unique book ... ." (Technometrics, Vol. 54 (2), May, 2003)