Selected Papers of Hirotugu Akaike
Springer Science & Business Media, 1998 - Mathematics - 434 pages
The pioneering research of Hirotugu Akaike has an international reputation for profoundly affecting how data and time series are analyzed and modelled and is highly regarded by the statistical and technological communities of Japan and the world. His 1974 paper "A new look at the statistical model identification" (IEEE Trans Automatic Control, AC-19, 716-723) is one of the most frequently cited papers in the area of engineering, technology, and applied sciences (according to a 1981 Citation Classic of the Institute of Scientific Information). It introduced the broad scientific community to model identification using the methods of Akaike's criterion AIC. The AIC method is cited and applied in almost every area of physical and social science. The best way to learn about the seminal ideas of pioneering researchers is to read their original papers. This book reprints 29 papers of Akaike's more than 140 papers. This book of papers by Akaike is a tribute to his outstanding career and a service to provide students and researchers with access to Akaike's innovative and influential ideas and applications. To provide a commentary on the career of Akaike, the motivations of his ideas, and his many remarkable honors and prizes, this book reprints "A Conversation with Hirotugu Akaike" by David F. Findley and Emanuel Parzen, published in 1995 in the journal Statistical Science. This survey of Akaike's career provides each of us with a role model for how to have an impact on society by stimulating applied researchers to implement new statistical methods.
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ABIC Akaike approach approximation AR-MA assumed assumption asymptotic autoregressive model Bayes procedure Bayesian model Bayesian statistics chi-square choice coefficients components considered controller design corresponding covariance covariance matrix defined definition denotes developed discussed entropy equation evaluation factor analysis feedback Findley Fisher frequency response function Gaussian given hypothesis identification information criterion Information Theory input Inst Institute of Statistical Japan least squares likelihood function likelihood principle linear MAICE manipulated variables Markovian representation Math matrix maximization maximum likelihood estimates multivariate number of parameters observation obtained optimal control outliers paper PID controller posterior probability power spectrum practical applications prediction error predictor present prior distribution problem random realized relation Research respect sample Science seasonal adjustment series analysis shows significant smoothing solution spectral density stationary Statistical Mathematics statistical model statisticians stochastic process Table theoretical tion Tokyo true distribution values variance vector zero