Proceedings of the First U.S./Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach Volume 1: Theory and Methodology of Time Series Analysis Volume 2: Multivariate Statistical Modeling Volume 3: Engineering and Scientific Applications

These three volumes comprise the proceedings of the US/Japan Conference, held in honour of Professor H. Akaike, on the `Frontiers of Statistical Modeling: an Informational Approach'. The major theme of the conference was the implementation of statistical modeling through an informational approach to complex, real-world problems.
Volume 1 contains papers which deal with the Theory and Methodology of Time Series Analysis. Volume 1 also contains the text of the Banquet talk by E. Parzen and the keynote lecture of H. Akaike. Volume 2 is devoted to the general topic of Multivariate Statistical Modeling, and Volume 3 contains the papers relating to Engineering and Scientific Applications.
For all scientists whose work involves statistics.

Volume 1
Editor's General Preface. Preface. 1. Hirotugu Akaike, Statistical Scientist; E. Parzen. 2. Experiences on the Development of Time Series Models (Keynote lecture); H. Akaike. 3. State Space Modeling of Time Series; G. Kitagawa. 4. Autoregressive Model Fitting and Windows; M.B. Priestley. 5. System Analysis and Seasonal Adjustment through Model Fitting; M. Ishiguro. 6. Akaike's Approach can Yield Consistent Order Determination; H. Tong. 7. Recursive Order Selection for an ARMA Process; R.J. Bhansali. 8. Autoregressive Model Selection in Small Samples using a Bias-Corrected Version of AIC; C.M. Hurvich, C.L. Tsai. 9. Temporal Causality Measures based on AIC; W. Polasek. 10. An Automated Robust Method for Estimating Trend and Detecting Changes in Trend for Short Time Series; T. Atilgan. 11. Model Selection in Harmonic Non-Linear Regression; D. Haughton, J. Haughton, A. Izenman. 12. Dynamic Analysis of Japan's Economic Structure; S. Naniwa. 13. New Estimates of the Autocorrelation Coefficients of Stationary Sequences; S. Batalama, D, Kazakos. 14. Applications of TIMSAC; Y. Tamura.
Volume 2
Editor's General Preface. Preface. 1. Some Aspects of Model-Selection Criteria; S.L. Sclove. 2. Mixture-Model Cluster Analysis using Model Selection Criteria and a new Informational Measure of Complexity; H. Bozdogan. 3. Information and Entropy in Cluster Analysis; H.H. Bock. 4. Information-Based Validity Functionals for Mixture Analysis; A.C. Cutler, M.P. Windham. 5. Unsupervised Classification with Stochastic Complexity; J. Rissanen, E.S. Ristad. 6. Modelling Principle Components with Structure; B.D. Flury, B. Neuenschwander. 7. AIC-Replacements for Some Multivariate Tests of Homogeneity with Applications in Multisample Clustering and Variable Selection; H. Bozdogan, S.L. Sclove, A.K. Gupta. 8. High Dimensional Covariance Estimation: Avoiding `The Curse of Dimensionality'; R.M. Pruzek. 9. Categorical Data Analysis by AIC; Y. Sakamoto. 10. Longitudinal Data Models with Fixed and Random Effects; R.H. Jones. 11. Multivariate Autoregressive Modeling for Analysis of Biomedical Systems with Feedback; T. Wada, T. Koyama, M. Shigemori. 12. A Simulation Study of Information Theoretic Techniques and Classical Hypothesis Tests in One Factor ANOVA; E.P. Rosenblum.
Volume 3
Editor's General Preface. Preface. 1. Implications of Informational Point of View on the Development of Statistical Science (Keynote lecture); H. Akaike. 2. From Comparison Density to Two Sample Analysis; E. Parzen. 3. Statistical Identification and Optimal Control of Thermal Power Plants; H. Nakamura. 4. Applications of Autoregressive Model to Control Ship's Motions and Marine Engine; K. Ohtsu, G. Kitagawa. 5. Statistical Models for Earthquake Occurrence: Clusters, Cycles and Characteristic Earthquakes; D. Vere-Jones. 6. Seismological Applications of Statistical Methods for Point-Process Modelling; Y. Ogata. 7. One Channel at-a-Time Multichannel Autoregressive Modeling of Stationary and Nonstationary Time Series; W. Gersch, D. Stone. 8. Separation of Spin Synchronized Signals using a Bayesian Approach; T. Higuchi. 9. The Local Linearization Filter with Application to Nonlinear System Identifications; T. Ozaki. 10. Inference of Evolutionary Trees from DNA and Protein Sequence Data; M. Hasegawa. 11. New Structure Criteria in Group Method of Data Handling; T. Lange. 12. The Use of the Kullback--Leibler Distance for Learning in Neural Binary Classifiers; D. Pados, P. Papantoni-Kazakos, D. Kazakos, A. Koyiantis. Index.