Time series analysis and its applications: with R examples
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY [u.a.]
Springer
2011
|
| Edition: | 3. ed. |
| Series: | Springer texts in statistics
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis Klappentext |
| Item Description: | Literaturverz. S. [577] - 589 |
| Physical Description: | XI, 596 S. graph. Darst. |
| ISBN: | 9781441978646 9781461427599 |
Staff View
MARC
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| 245 | 1 | 0 | |a Time series analysis and its applications |b with R examples |c Robert H. Shumway ; David S. Stoffer |
| 250 | |a 3. ed. | ||
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| adam_text |
Time Series Analysis and Its Applications presents a balanced and comprehensive
treatment of both time and frequency domain methods with accompanying theory.
Numerous examples using
nontrivial
data illustrate solutions to problems such as
discovering natural and anthropogenic climate change, evaluating pain perception
experiments using functional magnetic resonance imaging, and monitoring a nuclear
test ban treaty. The book is designed to be useful as a text for graduate level students
in the physical, biological and social sciences and as a graduate level text in statistics.
Some parts may also serve as an undergraduate introductory course. Theory and meth¬
odology are separated to allow presentations on different levels. In addition to coverage
of classical methods of time series regression, ARIMA models, spectral analysis and
state-space models, the text includes modern developments including categorical time
series analysis, multivariate spectral methods, long memory series, nonlinear models,
resampling techniques, GARCH models, stochastic volatility, wavelets and Markov
chain Monte Carlo integration methods.
The third edition includes a new section on testing for unit roots and the material on
state-space modeling, ARMAX models, and regression with autocorrelated errors has
been expanded. Also new to this edition is the enhanced use of the freeware statistical
package R. In particular,
R
code is now included in the text for nearly all of the numeri¬
cal examples. Data sets and additional
R
scripts are now provided in one file that may
be downloaded via the World Wide Web. This
R
supplement is a small compressed
file that can be loaded easily into
R
making all the data sets and scripts available to the
user with one simple command. The website for the text includes the code used in each
example so
ťhatthe
reader may simply copy-and-paste code directly into R. Appendix
R, which is new to this edition, provides a reference for the data sets and our
R
scripts
that
ałe
used throughout the text. In addition, Appendix
R
includes a tutorial on basic
R
commands as well as an
R
time series tutorial.
Contents
Preface
to the Third Edition
.
vii
1
Characteristics of Time Series
. 1
1.1
Introduction
. 1
1.2
The Nature of Time Series Data
. 3
1.3
Time Series Statistical Models
. 11
1.4
Measures of Dependence: Autocorrelation and
Cross-Correlation
. 17
1.5
Stationary Time Series
. 22
1.6
Estimation of Correlation
. 28
1.7
Vector-Valued and Multidimensional Series
. 33
Problems
. 39
2
Time Series Regression and Exploratory Data Analysis
. 47
2.1
Introduction
. 47
2.2
Classical Regression in the Time Series Context
. 48
2.3
Exploratory Data Analysis
. 57
2.4
Smoothing in the Time Series Context
. 70
Problems
. 78
3
ARIMA Models
. 83
3.1
Introduction
. 83
3.2
Autoregressive
Moving Average Models
. 84
3.3
Difference Equations
. 97
3.4
Autocorrelation and Partial Autocorrelation
.102
3.5
Forecasting
.108
3.6
Estimation
.121
3.7
Integrated Models for Nonstationary Data
.141
3.8
Building ARIMA Models
.144
3.9
Multiplicative Seasonal ARIMA Models
.154
Problems
.162
Contents
Spectral
Analysis and Filtering
.173
4.1
Introduction
.173
4.2
Cyclical Behavior and Periodicity
.175
4.3
The Spectral Density
.180
4.4
Periodogram
and Discrete Fourier Transform
.187
4.5
Nonparametric Spectral Estimation
.196
4.6
Parametric Spectral Estimation
.212
4.7
Multiple Series and Cross-Spectra
.216
4.8
Linear Filters
.221
4.9
Dynamic Fourier Analysis and Wavelets
.228
4.10
Lagged Regression Models
.242
4.11
Signal Extraction and Optimum Filtering
.247
4.12
Spectral Analysis of Multidimensional Series
.252
Problems
.255
Additional Time Domain Topics
.267
5.1
Introduction
.267
5.2
Long Memory
ARMA
and Fractional Differencing
.267
5.3
Unit Root Testing
.277
5.4
GAR.CH Models
.280
5.5
Threshold Models
.289
5.6
Regression with Autocorrelated Errors
.293
5.7
Lagged Regression: Transfer Function Modeling
.296
5.8
Multivariate ARM AX Models
.301
Problems
.315
State-Space Models
.319
6.1
Introduction
.319
6.2
Filtering, Smoothing, and Forecasting
.325
6.3
Maximum Likelihood Estimation
.335
6.4
Missing Data Modifications
.344
6.5
Structural Models: Signal Extraction and Forecasting
.350
6.6
State-Space Models with Correlated Errors
.354
6.6.1
ARMAX Models
.355
6.6.2
Multivariate Regression with Autocorrelated Errors
. 356
6.7
Bootstrapping State-Space Models
.359
6.8
Dynamic Linear Models with Switching
.365
6.9
Stochastic Volatility
.378
6.10
Nonlinear and Non-normal State-Space Models Using Monte
Carlo Methods
.387
Problems
.398
Contents xi
7
Statistical Methods in the Frequency Domain
.405
7.1
Introduction
.405
7.2
Spectral Matrices and Likelihood Functions
.409
7.3
Regression for Jointly Stationary Series
.410
7.4
Regression with Deterministic Inputs
.420
7.5
Random Coefficient Regression
.429
7.6
Analysis of Designed Experiments
.434
7.7
Discrimination and Cluster Analysis
.450
7.8
Principal Components and Factor Analysis
.468
7.9
The Spectral Envelope
.485
Problems
.501
Appendix A: Large Sample Theory
.507
A.I Convergence Modes
.507
A.
2
Central Limit Theorems
.515
A.3 The Mean and Autocorrelation Functions
.518
Appendix B: Time Domain Theory
.527
B.I Hubert Spaces and the Projection Theorem
.527
B.2 Causal Conditions for
ARMA
Models
.531
B.3 Large Sample Distribution of the AR,(p) Conditional Least
Squares Estimators
.533
B.4 The Wold Decomposition
.537
Appendix C: Spectral Domain Theory
.539
C.I Spectral Representation Theorem
.539
C.2 Large Sample Distribution of the DFT and Smoothed
Periodogram
.543
C.3 The Complex Multivariate Normal Distribution
.554
Appendix
R: R
Supplement
.559
R.I First Things First
.559
R.I.I Included Data Sets
.560
R.1.2 Included Scripts
.562
R.2 Getting Started
.567
R.3 Time Series Primer
.571
References
.577
Index
.591 |
| any_adam_object | 1 |
| author | Shumway, Robert H. Stoffer, David S. |
| author_GND | (DE-588)1050751000 |
| author_facet | Shumway, Robert H. Stoffer, David S. |
| author_role | aut aut |
| author_sort | Shumway, Robert H. |
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| building | Verbundindex |
| bvnumber | BV037370522 |
| classification_rvk | QH 237 SK 845 |
| classification_tum | MAT 634f |
| ctrlnum | (OCoLC)706945640 (DE-599)DNB1006927409 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 3. ed. |
| format | Book |
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| institution | BVB |
| isbn | 9781441978646 9781461427599 |
| language | English |
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| spellingShingle | Shumway, Robert H. Stoffer, David S. Time series analysis and its applications with R examples Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4067486-1 |
| title | Time series analysis and its applications with R examples |
| title_auth | Time series analysis and its applications with R examples |
| title_exact_search | Time series analysis and its applications with R examples |
| title_full | Time series analysis and its applications with R examples Robert H. Shumway ; David S. Stoffer |
| title_fullStr | Time series analysis and its applications with R examples Robert H. Shumway ; David S. Stoffer |
| title_full_unstemmed | Time series analysis and its applications with R examples Robert H. Shumway ; David S. Stoffer |
| title_short | Time series analysis and its applications |
| title_sort | time series analysis and its applications with r examples |
| title_sub | with R examples |
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Description
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 SK 845 S562(3) |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |