MODEL SELECTION STRATEGY IN THE ANALYSIS OF CAPTURE-RECAPTURE DATA

Analysis of capture-recapture data is critically dependent upon selection of a proper model for inference. Model selection is particularly important in the analysis of multiple, interrelated data sets. This paper evaluates information theoretic approaches to selection of a parsimonious model and compares them to the use of likelihood ratio tests using four cu levels. The purpose of the evaluation is to compare model selection strategies based on the quality of the inference, rather than on the degree to which differing selection strategies select the ''true model.'' A measure of squared bias and variance (termed RSS) is used as a basis for comparing different data-based selection strategies, assuming that a minimum RSS value is a reasonable target. In general, the information theoretic approaches consistently selected models with a smaller RSS than did the likelihood ratio testing approach. Two information theoretic criteria have a balance between underfitting and overfitting when compared to models where the average minimum RSS was known. Other findings are presented along with a discussion of the concept of a ''true model'' and dimension consistency in model selection.