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Hirotugu Akaike
Japanese statistician
Hirotugu Akaike (赤池 弘次, Akaike Hirotsugu, IPA:[akaikeçiɾotsɯɡɯ], November 5, 1927 – August 4, 2009) was a Japanese statistician.[1] 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.
Hirotugu akaike biography of martin
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