Synopses & Reviews
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.
Table of Contents
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.