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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

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TLDR
The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Abstract
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary

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Dynamic Response-by-Response Models of Matching Behavior in Rhesus Monkeys

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Promising the future? Global change projections of species distributions

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Failure to Recover to Baseline Pulmonary Function after Cystic Fibrosis Pulmonary Exacerbation

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