Multimodel Inference

Abstract

Hirotugu Akaike was born in 1927 in Fujinomiya-shi, Shizuoka-jen in Japan. He received B.S. and D.S. degrees in mathematics from the University of Tokyo in 1952 and 1961, respectively. He worked at the Institute of Statistical Mathematics for over 30 years, becoming its Director General in 1982. He has received many awards, prizes, and honors for his work in theoretical and applied statistics (de Leeuw 1992; Parzen 1994). This list includes the Asahi Prize, the Japanese Medal with Purple Ribbon, the Japan Statistical Society Award, and the 2006 Kyoto Prize. The three volume set entitled “Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach” (Bozdogan 1994) was to commemorate Professor Hirotugu Akaike’s 65th birthday. He is currently a Professor Emeritus at the Institute, a position he has held since 1994 and he received the Kyoto Prize in Basic Science in March, 2007.

For many years it seemed logical to somehow select the best model from an a priori set (but many people ran “all possible models”) and make inductive inferences from that best model. This approach has been the goal, for example, in regression analysis using AIC, Mallows’ (1973) C_p or step-up, step-down, or stepwise methods. Making inferences from the estimated best model seems logical and has served science for the past 50 years.