Information Criteria for the Paired-Comparisons Problem

Abstract Model-selection procedures such as Akaike's information criterion, AIC, provide the basis for an attractive alternative to traditional pairwise comparisons procedures such as the Tukey HSD procedure. These proposed procedures can be set up to avoid problems, such as intransitive decisions, associated with conducting a series of correlated significance tests. Furthermore, they can be formulated for heterogeneous variance cases, can be based on non-normal distributions, and can be generalized to multivariate settings. Both univariate and bivariate examples are presented with computations conducted in a spreadsheet environment. A small-scale simulation study provides some evidence concerning the all-pairs power and patterns of decision errors for model selection using information criteria.