Selected Papers of Hirotugu Akaike

ISBN-13: 9781461272489 / Angielski / Miękka / 2012 / 434 str.

Emanuel Parzen; Kunio Tanabe; Genshiro Kitagawa

Opis

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.

Spis treści

Foreword.- A Conversation with Hirotugu Akaike.- List of Publications of Hirotugu Akaike.- Papers.- 1. Precursors.- 1. On a zero-one process and some of its applications.- 2. On a successive transformation of probability distribution and its application to the analysis of the optimum gradient method.- 2. Frequency Domain Time Series Analysis.- 1. Effect of timing-error on the power spectrum of sampled-data.- 2. On a limiting process which asymptotically produces f-2 spectral density.- 3. On the statistical estimation of frequency response function.- 3. Time Domain Time Series Analysis.- 1. On the use of a linear model for the identification of feedback systems.- 2. Fitting autoregressive models for prediction.- 3. Statistical predictor identification.- 4. Autoregressive model fitting for control.- 5. Statistical approach to computer control of cement rotary kilns.- 6. Statistical identification for optimal control of supercritical thermal power plants.- 4. AIC and Parametrization.- 1. Information theory and an extension of the maximum likelihood princilple.- 2. A new look at the statistical model identification.- 3. Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes.- 4. Covariance matrix computation of the state variable of a stationary Gaussian process.- 5. Analysis of cross classified data by AIC.- 6. On linear intensity models for mixed doubly stochastic Poisson and self-exciting point processes.- 5. Bayesian Approach.- 1. A Baysian analysis of the minimum AIC procedure.- 2. A new look at the Bayes procedure.- 3. On the likelihood of a time series model.- 4. Likelihood and the Bayes procedure.- 5. Seasonal adjustment by a Bayesian modeling.- 6. A quasi Bayesian approach to outlier detection.- 7. On the fallacy of the likelihood principle.- 8. A Bayesian apporach to the analysis of earth tides.- 9. Factor analysis and AIC.- 6. General Views on Statistics.- 1. Prediction and entropy.- 2. Experiences on the development of time series models.- 3. Implications of informational point of view on the development of statistical science.

O autorze

Hirotugu Akaike is very well-known in the areas of statistics and engineering for his work in time series. He is best-known for the Akaike Information Criterion (AIC), a method for solving practical problems involved in fitting statistical models to data.