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Introduction to time series modeling with applications in R / (Record no. 38193)

MARC details
000 -LEADER
fixed length control field	02778cam a22002058i 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN	9780367494247
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title	eng-
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.55
Item number	KIT-I
100 1# - MAIN ENTRY--AUTHOR NAME
Personal name	Kitagawa, G.
Fuller form of name	(Genshiro),
Relator term	author.
245 10 - TITLE STATEMENT
Title	Introduction to time series modeling with applications in R /
Statement of responsibility, etc	Genshiro Kitagawa.
250 ## - EDITION STATEMENT
Edition statement	2nd edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication	UK :
Name of publisher	CRC,
Year of publication	2022.
300 ## - PHYSICAL DESCRIPTION
Number of Pages	340p.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc	Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc	"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 discover for 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"--
546 ## - LANGUAGE NOTE
Language note	English.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term	State-space methods.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term	Time-series analysis.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Books

Holdings
Withdrawn status	Lost status	Damaged status	Not for loan	Home library	Current library	Date acquired	Source of acquisition	Cost, normal purchase price	Full call number	Accession Number	Cost, replacement price	Price effective from	Koha item type
				NASSDOC Library	NASSDOC Library	16/03/2023	Overseas	0.00	519.55 KIT-I	52809	0.00	29/05/2023	Books