Erin Blankenship : abstract

The Information-Theoretic Approach to Model Selection: Description and Case Study
Erin Blankenship
Department of Statistics
University of Nebraska - Lincoln
In the wildlife literature there has been some recent criticism of statistical significance testing. In the past few years, both the Journal of Wildlife Management and the Wildlife Society Bulletin have published articles criticizing the overuse and misuse of hypothesis tests. One alternative to using hypothesis tests for model selection is the information-theoretic approach, proposed by Burnham and Anderson (1998). This technique uses values such as the Akaike Information Criterion, Schwarz's Bayesian Information Criterion and others to choose a set of plausible models from a set of a priori candidate models. Inferences are based on the set of plausible models, rather than on a single selected best model, and model-averaged point estimates of parameters may be used for the wildlife science community, and statisticians who work with wildlife scientists should be aware of this analysis technique. This talk will introduce statisticians and mathematicians to the information-theoretic approach to model selection and the statistical theory underlying it, as well as demonstrate the technique using data on bird species richness and abundance in riparian areas in southeastern Nebraska.