Burnham K.P., Anderson D.R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach

New York: Springer-Verlag, 2002. — 488 pp. Second EditionWe 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 (multimodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book.