Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
Fred S. Guthery low, Kenneth P. Burnham, David Anderson
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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| Paper | Year | Cited |
|---|---|---|
| Multimodel Inference | 2004 | 11,781 |
| AIC model selection using Akaike weights | 2004 | 2,835 |
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| Neural Networks and Applications | Computer Science |
| Statistical and Computational Modeling | Computer Science |
| Scientific Measurement and Uncertainty Evaluation | Decision Sciences |
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