Kitagawa, G. (Genshiro) 1948
Overview
Works:  70 works in 212 publications in 2 languages and 3,120 library holdings 

Genres:  Conference papers and proceedings 
Roles:  Author, Editor, Other 
Publication Timeline
.
Most widely held works by
G Kitagawa
Information criteria and statistical modeling by
Sadanori Konishi(
)
22 editions published between 2007 and 2011 in English and Undetermined and held by 638 WorldCat member libraries worldwide
The Akaike information criterion (AIC) derived as an estimator of the KullbackLeibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz's Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach
22 editions published between 2007 and 2011 in English and Undetermined and held by 638 WorldCat member libraries worldwide
The Akaike information criterion (AIC) derived as an estimator of the KullbackLeibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz's Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach
Methods and applications of signal processing in seismic network operations by
Tetsuo Takanami(
Book
)
11 editions published between 2002 and 2003 in English and held by 289 WorldCat member libraries worldwide
Methods and applications of signal processing in seismic network operations are described by the specialists in the fields of signal processing problems in seismology, in particular in the the current seismological networks world. This book addresses successful applications of signal processing in the seismic networks and the time series analysis. The practices included in this book are also related to various stages of development of seismic networks and time series analysis. This book is a valuable handbook for seismologists and engineers working in the seismic network as well as being a reference book for university level researchers in the field of signal processing in seismology
11 editions published between 2002 and 2003 in English and held by 289 WorldCat member libraries worldwide
Methods and applications of signal processing in seismic network operations are described by the specialists in the fields of signal processing problems in seismology, in particular in the the current seismological networks world. This book addresses successful applications of signal processing in the seismic networks and the time series analysis. The practices included in this book are also related to various stages of development of seismic networks and time series analysis. This book is a valuable handbook for seismologists and engineers working in the seismic network as well as being a reference book for university level researchers in the field of signal processing in seismology
Indexation and causation of financial markets : nonstationary time series analysis method by
Yoko Tanokura(
)
12 editions published between 2015 and 2016 in English and held by 280 WorldCat member libraries worldwide
This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavytailed and investigates the effectiveness of the method. In order to fully reflect the movements of prices or returns on a financial asset, the index should reflect their distributions. However, they are often heavytailed and possibly skewed, and identifying them directly is not easy. This book first develops an index construction method depending on the price distributions, by using nonstationary time series analysis. Firstly, the longterm trend of the distributions of the optimal Boxℓ́ℓCox transformed prices is estimated by fitting a trend model with timevarying observation noises. By applying state space modeling, the estimation is performed and missing observations are automatically interpolated. Finally, the index is defined by taking the inverse Boxℓ́ℓCox transformation of the optimal longterm trend. This book applies the method to various financial data. For example, applying it to the sovereign credit default swap market where the number of observations varies over time due to the immaturity, the spillover effects of the financial crisis are detected by using the power contribution analysis measuring the information flows between indices. The investigations show that applying this method to the markets with insufficient information such as fastgrowing or immature markets can be effective.
12 editions published between 2015 and 2016 in English and held by 280 WorldCat member libraries worldwide
This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavytailed and investigates the effectiveness of the method. In order to fully reflect the movements of prices or returns on a financial asset, the index should reflect their distributions. However, they are often heavytailed and possibly skewed, and identifying them directly is not easy. This book first develops an index construction method depending on the price distributions, by using nonstationary time series analysis. Firstly, the longterm trend of the distributions of the optimal Boxℓ́ℓCox transformed prices is estimated by fitting a trend model with timevarying observation noises. By applying state space modeling, the estimation is performed and missing observations are automatically interpolated. Finally, the index is defined by taking the inverse Boxℓ́ℓCox transformation of the optimal longterm trend. This book applies the method to various financial data. For example, applying it to the sovereign credit default swap market where the number of observations varies over time due to the immaturity, the spillover effects of the financial crisis are detected by using the power contribution analysis measuring the information flows between indices. The investigations show that applying this method to the markets with insufficient information such as fastgrowing or immature markets can be effective.
The practice of time series analysis(
Book
)
10 editions published between 1998 and 1999 in English and held by 277 WorldCat member libraries worldwide
This book presents a collection of applied papers on time series that have not appeared in English. The applications are primarily to engineering and the physical sciences
10 editions published between 1998 and 1999 in English and held by 277 WorldCat member libraries worldwide
This book presents a collection of applied papers on time series that have not appeared in English. The applications are primarily to engineering and the physical sciences
Smoothness priors analysis of time series by
G Kitagawa(
Book
)
12 editions published in 1996 in English and held by 276 WorldCat member libraries worldwide
12 editions published in 1996 in English and held by 276 WorldCat member libraries worldwide
Introduction to time series modeling by
G Kitagawa(
Book
)
14 editions published in 2010 in English and held by 270 WorldCat member libraries worldwide
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f
14 editions published in 2010 in English and held by 270 WorldCat member libraries worldwide
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f
Time series modeling for analysis and control : advanced autopilot and monitoring systems by
Kohei Ohtsu(
)
9 editions published in 2015 in English and held by 263 WorldCat member libraries worldwide
This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships' autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function nettype coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for coursekeeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, routetracking controllers by direct steering, and the reference coursesetting approach. The methods presented here are exemplified with real data analysis and experiments on real ships. This book is highly recommended to readers who are interested in designing optimal or adaptive controllers not only of ships but also of any other complicated systems under noisy disturbance conditions. 
9 editions published in 2015 in English and held by 263 WorldCat member libraries worldwide
This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships' autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function nettype coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for coursekeeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, routetracking controllers by direct steering, and the reference coursesetting approach. The methods presented here are exemplified with real data analysis and experiments on real ships. This book is highly recommended to readers who are interested in designing optimal or adaptive controllers not only of ships but also of any other complicated systems under noisy disturbance conditions. 
Akaike information criterion statistics by
Y Sakamoto(
Book
)
17 editions published between 1983 and 1987 in English and Undetermined and held by 238 WorldCat member libraries worldwide
17 editions published between 1983 and 1987 in English and Undetermined and held by 238 WorldCat member libraries worldwide
Selected papers of Hirotugu Akaike by
Hirotsugu Akaike(
Book
)
7 editions published in 1998 in English and held by 166 WorldCat member libraries worldwide
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" is one of the most frequently cited papers in the area of engineering, technology, and applied sciences. 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. The slected papers are divided into six groups, represent successive phases of Akaike's research interests during his more than 40 years of work at the prestigious Institute of Statistical Mathematical in Tokyo
7 editions published in 1998 in English and held by 166 WorldCat member libraries worldwide
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" is one of the most frequently cited papers in the area of engineering, technology, and applied sciences. 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. The slected papers are divided into six groups, represent successive phases of Akaike's research interests during his more than 40 years of work at the prestigious Institute of Statistical Mathematical in Tokyo
Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: an Informational Approach by
H Bozdogan(
)
9 editions published in 1994 in English and held by 158 WorldCat member libraries worldwide
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, realworld 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
9 editions published in 1994 in English and held by 158 WorldCat member libraries worldwide
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, realworld 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
Selected Papers of Hirotugu Akaike by
Emanuel Parzen(
)
3 editions published in 1998 in English and held by 56 WorldCat member libraries worldwide
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" is one of the most frequently cited papers in the area of engineering, technology, and applied sciences. 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. The slected papers are divided into six groups, represent successive phases of Akaike's research interests during his more than 40 years of work at the prestigious Institute of Statistical Mathematical in Tokyo
3 editions published in 1998 in English and held by 56 WorldCat member libraries worldwide
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" is one of the most frequently cited papers in the area of engineering, technology, and applied sciences. 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. The slected papers are divided into six groups, represent successive phases of Akaike's research interests during his more than 40 years of work at the prestigious Institute of Statistical Mathematical in Tokyo
Smoothness Priors Analysis of Time Series by
G Kitagawa(
)
1 edition published in 1996 in English and held by 47 WorldCat member libraries worldwide
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distributiontwo filter smoothing formula, and a Monte Carlo "particlepath tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures
1 edition published in 1996 in English and held by 47 WorldCat member libraries worldwide
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distributiontwo filter smoothing formula, and a Monte Carlo "particlepath tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures
Introduction to time series modeling by
G Kitagawa(
Book
)
3 editions published in 2010 in English and held by 19 WorldCat member libraries worldwide
3 editions published in 2010 in English and held by 19 WorldCat member libraries worldwide
Akaike information criterion statistics by
Yoshikazu Sakamoto(
Book
)
1 edition published in 1986 in English and held by 14 WorldCat member libraries worldwide
1 edition published in 1986 in English and held by 14 WorldCat member libraries worldwide
OUTLAP, an outlier analysis program by
G Kitagawa(
Book
)
3 editions published in 1980 in English and held by 7 WorldCat member libraries worldwide
3 editions published in 1980 in English and held by 7 WorldCat member libraries worldwide
Proceedings of the first US(
)
1 edition published in 1994 in English and held by 6 WorldCat member libraries worldwide
1 edition published in 1994 in English and held by 6 WorldCat member libraries worldwide
Special issue on nonlinear nongaussian models and related filtering methods(
Book
)
1 edition published in 2001 in English and held by 6 WorldCat member libraries worldwide
1 edition published in 2001 in English and held by 6 WorldCat member libraries worldwide
Introduction to time series modeling by
G Kitagawa(
Book
)
1 edition published in 2010 in English and held by 5 WorldCat member libraries worldwide
1 edition published in 2010 in English and held by 5 WorldCat member libraries worldwide
Māketingu no kagaku : POS dēta no kaiseki(
Book
)
1 edition published in 2005 in Japanese and held by 4 WorldCat member libraries worldwide
1 edition published in 2005 in Japanese and held by 4 WorldCat member libraries worldwide
Introduction to time series modeling by
G Kitagawa(
Book
)
3 editions published in 2010 in English and held by 4 WorldCat member libraries worldwide
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f
3 editions published in 2010 in English and held by 4 WorldCat member libraries worldwide
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f
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Related Identities
 Konishi, Sadanori Author
 Akaike, Hirotsugu 19272009 Other Author Editor
 Gersch, Will Author
 Takanami, Tetsuo 1945 Author
 Tanokura, Yoko Author
 Ohtsu, Kohei Author
 Peng, Hui
 Ishiguro, M. (Makio) 1946
 Sakamoto, Y. (Yosiyuki) 1943 Author
 Tanabe, Kunio 1941 Other Editor
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Associated Subjects
Analysis of variance Bayesian statistical decision theory Bioinformatics Capital marketStatistical methods Computer science Computer simulation Data mining Distribution (Probability theory) Econometrics EngineeringStatistical methods Feedback control systemsMathematical models Global analysis (Mathematics) Information modeling Mathematical analysis Mathematical statistics Mathematics Multivariate analysis Physical geography Price indexes SeismologyMethodology Signal processingDigital techniques Statespace methods Statistics Stochastic analysis Timeseries analysis Timeseries analysisData processing Timeseries analysisMathematical models