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.
Table Of Contents:PrefaceAbout the AuthorsGlossary1Introduction12Information and Likelihood Theory: A Basis for Model Selection and Inference493Basic Use of the Information-Theoretic Approach984Formal Inference From More Than One Model: Multimodel Inference (MMI)1495Monte Carlo Insights and Extended Examples2066Advanced Issues and Deeper Insights2677Statistical Theory and Numerical Results3528Summary437References455Index485
|Book:||Model Selection And Multi-Model Inference|
|Author:||David Anderson Kenneth P. Burnham|
|Number of Pages:||496|
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