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19 produkter

Introduction to Time Series Modeling with Applications in R
AvGenshiro Kitagawa
Inbunden, Engelska, 2020
2 060 kr
Skickas inom 10-15 vardagar
Praise for the first edition:[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in MedicineWhat distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters.–MAA ReviewsIntroduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.About the Author:Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
Introduction to Time Series Modeling with Applications in R
AvGenshiro Kitagawa
Häftad, Engelska, 2022
766 kr
Skickas inom 10-15 vardagar
Praise for the first edition:[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in MedicineWhat distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters.–MAA ReviewsIntroduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.About the Author:Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
Information Criteria and Statistical Modeling
AvSadanori Konishi,Genshiro Kitagawa
Inbunden, Engelska, 2007
1 517 kr
Skickas inom 10-15 vardagar
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler 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.
Information Criteria and Statistical Modeling
AvGenshiro Kitagawa,Sadanori Konishi
E-bok
PDF, Engelska, 2007
1 413 kr
Läs direkt efter köp
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler 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.
Del 116 - Lecture Notes in Statistics
Smoothness Priors Analysis of Time Series
AvGenshiro Kitagawa,Will Gersch
Häftad, Engelska, 1996
1 409 kr
Skickas inom 10-15 vardagar
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 distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Selected Papers of Hirotugu Akaike
AvEmanuel Parzen,Kunio Tanabem. fl.
Inbunden, Engelska, 1997
2 165 kr
Skickas inom 10-15 vardagar
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.
Introduction to Time Series Modeling with Applications in R
AvGenshiro Kitagawa
E-bok
Engelska, 2020
906 kr
Läs direkt efter köp
Praise for the first edition:
[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework.
–Statistics in Medicine
What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters.
–MAA Reviews
Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.
This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.
Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.
About the Author:
Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
Introduction to Time Series Modeling with Applications in R
AvGenshiro Kitagawa
E-bok
PDF, Engelska, 2020
858 kr
Läs direkt efter köp
Praise for the first edition:
[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. … [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework.
–Statistics in Medicine
What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters.
–MAA Reviews
Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal of this book is to enable readers to build their own models to understand, predict and master time series. The second edition makes it possible for readers to reproduce examples in this book by using the freely available R package TSSS to perform computations for their own real-world time series problems.
This book employs the state-space model as a generic tool for time series modeling and presents the Kalman filter, the non-Gaussian filter and the particle filter as convenient tools for recursive estimation for state-space models. Further, it also takes a unified approach based on the entropy maximization principle and employs various methods of parameter estimation and model selection, including the least squares method, the maximum likelihood method, recursive estimation for state-space models and model selection by AIC.
Along with the standard stationary time series models, such as the AR and ARMA models, the book also introduces nonstationary time series models such as the locally stationary AR model, the trend model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models.
About the Author:
Genshiro Kitagawa is a project professor at the University of Tokyo, the former Director-General of the Institute of Statistical Mathematics, and the former President of the Research Organization of Information and Systems.
Information Criteria and Statistical Modeling
AvSadanori Konishi,Genshiro Kitagawa
Häftad, Engelska, 2010
1 086 kr
Skickas inom 10-15 vardagar
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler 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.
Smoothness Priors Analysis of Time Series
AvWill Gersch,Genshiro Kitagawa
E-bok
PDF, Engelska, 2012
1 728 kr
Läs direkt efter köp
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 distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Selected Papers of Hirotugu Akaike
AvGenshiro Kitagawa,Kunio Tanabem. fl.
E-bok
PDF, Engelska, 2012
2 769 kr
Läs direkt efter köp
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.
Practice of Time Series Analysis
AvGenshiro Kitagawa,Hirotugu Akaike
E-bok
PDF, Engelska, 2012
710 kr
Läs direkt efter köp
Due to the introduction of the information criterion AIC and development of prac tical use of Bayesian modeling, the method of time analysis is now showing remarkable progress. In attempting the study of a new field the actual phenomenon is rarely so simple as to allow direct applications of existing methods of analysis or models. The real thrill of the statistical analysis lies in the process of developing a new model depending on the purpose and the characteristics of the object of the research. The purpose of this book ist.o introduce the readers to successful applications of the meth ods of time series analysis in a variety of fields, such as engineering, earth science, medical science, biology, and economics. The editors have been aware of the importance of cooperative research in sta tistical science and carried out various cooperative research projects in the area of time series analysis. The Institute of Statistical Mathematics was reorganized as an inter-university research institute in 1985 and the activities of the Institute have been organized to promote the cooperative researches as its central activity. This book is composed of the outcomes of cooperative researches developed within this environ ment and contains the results ranging from the pioneering realizations of statistical control to the latest consequences of time series modeling.
Selected Papers of Hirotugu Akaike
AvEmanuel Parzen,Kunio Tanabem. fl.
Häftad, Engelska, 2012
2 488 kr
Skickas inom 10-15 vardagar
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.
Practice of Time Series Analysis
AvHirotugu Akaike,Genshiro Kitagawa
Häftad, Engelska, 2011
546 kr
Skickas inom 10-15 vardagar
Due to the introduction of the information criterion AIC and development of prac tical use of Bayesian modeling, the method of time analysis is now showing remarkable progress. In attempting the study of a new field the actual phenomenon is rarely so simple as to allow direct applications of existing methods of analysis or models. The real thrill of the statistical analysis lies in the process of developing a new model depending on the purpose and the characteristics of the object of the research. The purpose of this book ist.o introduce the readers to successful applications of the meth ods of time series analysis in a variety of fields, such as engineering, earth science, medical science, biology, and economics. The editors have been aware of the importance of cooperative research in sta tistical science and carried out various cooperative research projects in the area of time series analysis. The Institute of Statistical Mathematics was reorganized as an inter-university research institute in 1985 and the activities of the Institute have been organized to promote the cooperative researches as its central activity. This book is composed of the outcomes of cooperative researches developed within this environ ment and contains the results ranging from the pioneering realizations of statistical control to the latest consequences of time series modeling.
Del 98 - Lecture Notes in Earth Sciences
Methods and Applications of Signal Processing in Seismic Network Operations
AvTetsuo Takanami,Genshiro Kitagawa
Häftad, Engelska, 2002
1 086 kr
Skickas inom 10-15 vardagar
This book deals with various theoretical and practical methods for real-time automatic signal processing in local (and regional) seismic networks and associated software developments, including extraction of small seismic signal from noisy observation by piecewise modeling and self-organizing state space modeling, determination of arrival time of S wave by locally multivariate stationary AT modeling, automatic interpretation of seismic signal by combining cumulativ sum and simulative annealing (CUSUM-SA), AR-filtering for local and teleseismic events, the currently high sensitivity seismic network running in Japan (Hi-net), PC-based computer package for automatic detection and location of earthquakes, real-time automatic seismic data-processing in seismic network running in eastern Sicily (Italy), the SIL (South Iceland Lowland) seismological data acquisition system and routine analysis in Iceland and Sweden.
Indexation and Causation of Financial Markets
AvYoko Tanokura,Genshiro Kitagawa
Häftad, Engelska, 2016
546 kr
Skickas inom 10-15 vardagar
This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavy-tailed 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 heavy-tailed 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 long-term trend of the distributions of the optimal Box–Cox transformed prices is estimated by fitting a trend model with time-varying 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 Box–Cox transformation of the optimal long-term 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 fast-growing or immature markets can be effective.
Indexation and Causation of Financial Markets
AvGenshiro Kitagawa,Yoko Tanokura
E-bok
PDF, Engelska, 2016
710 kr
Läs direkt efter köp
This book presents a new statistical method of constructing a price index of a financial asset where the price distributions are skewed and heavy-tailed 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 heavy-tailed 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 long-term trend of the distributions of the optimal Box–Cox transformed prices is estimated by fitting a trend model with time-varying 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 Box–Cox transformation of the optimal long-term 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 fast-growing or immature markets can be effective.
Time Series Modeling for Analysis and Control
Advanced Autopilot and Monitoring Systems
AvKohei Ohtsu,Hui Pengm. fl.
Häftad, Engelska, 2015
546 kr
Skickas inom 10-15 vardagar
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 net-type 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 course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracking controllers by direct steering, and the reference course-setting 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.
Time Series Modeling for Analysis and Control
Advanced Autopilot and Monitoring Systems
AvGenshiro Kitagawa,Hui Pengm. fl.
E-bok
PDF, Engelska, 2015
710 kr
Läs direkt efter köp
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 net-type 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 course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracking controllers by direct steering, and the reference course-setting 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.

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Genshiro Kitagawa – författare

Introduction to Time Series Modeling with Applications in R

Introduction to Time Series Modeling with Applications in R

Information Criteria and Statistical Modeling

Information Criteria and Statistical Modeling

Smoothness Priors Analysis of Time Series

Selected Papers of Hirotugu Akaike

Introduction to Time Series Modeling with Applications in R

Introduction to Time Series Modeling with Applications in R

Information Criteria and Statistical Modeling

Smoothness Priors Analysis of Time Series

Selected Papers of Hirotugu Akaike

Practice of Time Series Analysis

Selected Papers of Hirotugu Akaike

Practice of Time Series Analysis

Methods and Applications of Signal Processing in Seismic Network Operations

Indexation and Causation of Financial Markets

Indexation and Causation of Financial Markets

Time Series Modeling for Analysis and Control

Time Series Modeling for Analysis and Control

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