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Model selection and multimodel inference : a practical information-theoretic approach
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). A philosophy is
Multimodel Inference
Various facets of such multimodel inference are presented here, particularly methods of model averaging, which can be derived as a non-Bayesian result.
Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies
This paper synthesizes, using a common framework, recent developments of capture-recapture models oriented to estimation of survival rates together with new ones, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets.
Model selection and multimodel inference
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Model selection and inference : a practical information-theoretic approach
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
Distance Sampling: Estimating Abundance of Biological Populations
This book presents a meta-modelling framework for estimating the probability of detection on the line or point in the context of tuna vessel observer data to assess trends in abundance of dolphins in the North Atlantic.
AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons
We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set