Time series: a data analysis approach using R (Record no. 397913)

MARC details
000 -LEADER
fixed length control field 04869 a2200181 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191007b 2019 ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780367221096
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5502855133
Item number S4T4
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Shumway, Robert H.
9 (RLIN) 386218
245 ## - TITLE STATEMENT
Title Time series: a data analysis approach using R
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. CRC Press
Place of publication, distribution, etc. Boca Raton
Date of publication, distribution, etc. 2019
300 ## - PHYSICAL DESCRIPTION
Extent xii, 259 p.
Other physical details Includes bibliographical references and index
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Texts in statistical science
9 (RLIN) 366377
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Table of Contents<br/>1. Time Series Elements<br/> Introduction <br/> Time Series Data <br/> Time Series Models <br/> Problems <br/><br/>2. Correlation and Stationary Time Series<br/> Measuring Dependence <br/> Stationarity <br/> Estimation of Correlation <br/>Problems <br/><br/>3. Time Series Regression and EDA<br/> Ordinary Least Squares for Time Series <br/> Exploratory Data Analysis <br/> Smoothing Time Series <br/> Problems <br/><br/>4. ARMA Models<br/> Autoregressive Moving Average Models <br/> Correlation Functions <br/> Estimation <br/> Forecasting <br/> Problems <br/><br/>5. ARIMA Models<br/> Integrated Models <br/> Building ARIMA Models <br/> Seasonal ARIMA Models <br/> Regression with Autocorrelated Errors * <br/> Problems <br/><br/>6. Spectral Analysis and Filtering<br/> Periodicity and Cyclical Behavior <br/> The Spectral Density <br/> Linear Filters * <br/> Problems <br/><br/>7. Spectral Estimation<br/> Periodogram and Discrete Fourier Transform <br/> Nonparametric Spectral Estimation <br/> Parametric Spectral Estimation <br/> Coherence and Cross-Spectra * <br/> Problems <br/><br/>8. Additional Topics *<br/> GARCH Models <br/> Unit Root Testing <br/> Long Memory and Fractional Differencing <br/> State Space Models <br/> Cross-Correlation Analysis and Prewhitening <br/> Bootstrapping Autoregressive Models <br/> Threshold Autoregressive Models <br/> Problems <br/><br/>Appendix A R Supplement<br/>Installing R <br/>Packages and ASTSA <br/>Getting Help <br/>Basics <br/>Regression and Time Series Primer <br/>Graphics <br/><br/>Appendix B Probability and Statistics Primer<br/>Distributions and Densities <br/>Expectation, Mean and Variance <br/>Covariance and Correlation <br/>Joint and Conditional Distributions <br/><br/>Appendix C Complex Number Primer<br/>Complex Numbers <br/>Modulus and Argument <br/>The Complex Exponential Function <br/>Other Useful Properties <br/>Some Trigonometric Identities <br/><br/>Appendix D Additional Time Domain Theory<br/>MLE for an AR() <br/>Causality and Invertibility <br/>ARCH Model Theory <br/><br/>Hints for Selected Exercises<br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc. The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.<br/><br/>Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software.<br/><br/>https://www.crcpress.com/Time-Series-A-Data-Analysis-Approach-Using-R/Shumway-Stoffer/p/book/9780367221096
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Stoffer, David S.
Relator term Co author
9 (RLIN) 386219
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction Ahmedabad Ahmedabad General Stacks 18/11/2019 6 4.00   519.5502855133 S4T4 200181 21/11/2019 5070.08 03/10/2019 Book
            Raipur Raipur   27/02/2021 BBC     519.55 SHU-19 IIMRP-11727 05/07/2022   05/07/2022 Book

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