Hirotugu Akaike

was a Japanese statistician. In the early 1970s, he formulated the Akaike information criterion (AIC). AIC is now widely used for model selection, which is commonly the most difficult aspect of statistical inference; additionally, AIC is the basis of a paradigm for the foundations of statistics. Akaike also made major contributions to the study of time series. As well, he had a large role in the general development of statistics in Japan. Akaike information criterion The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection. AIC was first formally described in a research paper by . As of October 2014, the paper had received more than 14000 citations in the Web of Science: making it the 73rd most-cited research paper of all time. (As of April 2016, the paper had received about 17000 citations.) Nowadays, AIC has become common enough that it is often used without citing Akaike's 1974 paper. Indeed, there are over 170,000 scholarly articles/books that use AIC (as assessed by Google Scholar).