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Название: Model Selection and Multimodel Inference: A Practical Information-theoretic Approach
Авторы: Burnham K.P., Anderson D.
We wrote this book to introduce graduate students and research workers in various scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a "best" model and a ranking and weighting of the remaining models in a pre-defined set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (mul-timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book.