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 presented for model-based data analysis and a general strategy outlined for the analysis of empirical data. The book invites increased attention on a priori science hypotheses and modeling. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected as an estimator of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These methods are relatively simple and easy to use in practice, but based on deep statistical theory. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems. The book presents several new ways to incorporate model selection uncertainty into parameter estimates and estimates of precision. An array of challenging examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians wanting to make inferences from multiple models and is suitable as a graduate text or as a reference for professional analysts.

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

MAXIMUM JOY

Anyone who has raised a child understands the difference between relationship and fellowship. A parent has a permanent relationship with his/her child. But fellowship with a child means the parent and the child are enjoying their relationship. And though their relationship is eternal, their fellowship is not. A father and his son have a permanent relationship, but unless they spend time together sharing with one another, they will not have much fellowship. The apostle John wrote one book about how to have an eternal relationship with God the Gospel of John (John 20:31). And he wrote another book about how to enjoy that relationship, or how to have fellowship with God First John (1 John 1:3-4). Dr. Anderson explains that First John is not about whether one has an eternal relationship with God, but it is about how to enjoy that relationship by having fellowship with God. And enjoying fellowship with God is what Dr. Anderson calls MAXIMUM JOY.