This 2-day session introduces a variety of general analysis methods based on Kullback-Leibler information. These new methods are useful in model-based inference in the analysis of empirical data in the sciences. The material focuses on science hypotheses, models, and model selection methods such as AIC, AICc, and QAICc. After introducing important background material, methods are introduced to make formal statistical inference from more than a single model (multimodel inference). These approaches include types of model averaging, incorporating model selection uncertainty into estimates of precision, dealing with model selection bias, ranking the importance of predictor variables, and confidence sets on models. The material is not deeply mathematical; the emphasis is on science concepts and philosophy and many examples are used to aid applications. These are informal sessions with substantial time for discussion and debate and getting side-tracked onto interesting related issues. This is not a course in how to derive mathematical models to represent various science hypohteses or management positions, although many examples will be provided relating to these issues. Likewise, it is not a course on estimation methods for model parameters, however, some time will be spent outlining the behind least squares and maximum concepts likelihood estimation. |
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