Time series analysis: methods and applications
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2012
|
| Ausgabe: | 1. ed. |
| Schriftenreihe: | Handbook of statistics
30 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 755 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9780444538581 0444538585 |
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| 245 | 1 | 0 | |a Time series analysis |b methods and applications |c ed. by Tata Subba Rao ... |
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Datensatz im Suchindex
| _version_ | 1804149215883952128 |
|---|---|
| adam_text | Table
of
Contents
Volume
30
Time Series
Preface to Handbook
- 30 xiii
Contributors: Vol.
30 xvii
Part I. Bootstrap and Tests for Linearity of a Time Series
Ch.
1.
Bootstrap Methods for Time Series
3
Jens-Peter Kreiss and Soumendra Nath Lahiri
1.
Introduction
3
2.
Residual bootstrap for parametric and nonparametric models
6
3.
Autoregressive-sieve bootstrap
9
4.
Bootstrap for Markov chains
11
5.
Block bootstrap methods
13
6.
Frequency domain bootstrap methods
16
7.
Mixture of two bootstrap methods
17
8.
Bootstrap under long-range dependence
21
Acknowledgment
23
References
23
Ch.
2.
Testing Time Series Linearity: Traditional and Bootstrap
Methods
27
Arthur Berg, Timothy McMurry and Dimitris
N.
Politis
1.
Introduction
27
2.
A brief survey of linearity and Gaussianity tests
28
3.
Linear and nonlinear time series
30
4.
AR-sieve bootstrap tests of linearity
33
5.
Subsampling
tests of linearity
35
References
40
vi
Table of
Contenta
Ch.
3.
The Quest for
Nonlinearity
in Time Series
43
Simone Giannerini
1.
Introduction
43
2.
Defining a linear process
45
3.
Testing for nonlinearity
48
4.
Conclusions
59
Acknowledgments
60
References
60
Part II. Nonlinear Time Series
65
Ch.
4.
Modelling Nonlinear and Nonstationary Time Series
67
Dag Tj0stheim
1.
Introduction
67
2.
Nonlinear stationary models
68
3.
Linear nonstationarity
75
4.
Nonlinear and nonstationary processes
79
5.
Time-varying parameters and state-space models
90
References
93
Ch.
5.
Markov Switching Time Series Models
99
Jürgen Franke
1.
Introduction
99
2.
Markov switching autoregressions
101
3.
Other Markov switching time series models
117
4.
Markov switching in continuous time
118
Acknowledgments
119
References
120
Ch.
6.
A Review of Robust Estimation under Conditional
Heteroscedasticity
123
Kanchan Mukherjee
1.
Introduction
123
2.
GARCH
(/;,<:/)
and GJR
(1,1)
models
125
3.
Data analysis for the GARCH and GJR models
131
4.
Value at risk and M-tests
134
5.
Data analysis based on VaR
137
6.
Nonlinear AR-ARCH model
142
7.
Data analysis for the AR-ARCH model
150
8.
Conclusions
153
Acknowledgments
153
References
153
Table
of Contents
Part III. High Dimensional Time Series
155
Ch.
7.
Functional Time Series
157
Siegfried
Hörmann
and
Piotr Kokoszka
1.
Introduction
157
2.
The Hubert space model for functional data
160
3.
Functional
autoregressive
model
166
4.
Weakly dependent functional time series
175
5.
Further reading
184
Acknowledgments
184
References
185
Ch.
8.
Covariance Matrix Estimation in Time Series
187
Wei Biao Wu and Han Xiao
1.
Introduction
187
2.
Asymptotics of sample covariances
189
3.
Low-dimensional covariance matrix estimation
193
4.
High-dimensional covariance matrix estimation
200
Acknowledgments
206
References
206
Part IV. Time Series and Quantile Regression
211
Ch.
9.
Time Series Quantile Regressions
213
Zhijie Xiao
1.
An introduction to quantile regression
213
2.
Quantile regression for
autoregressive
time series
215
3.
Quantile regression for ARCH and GARCH models
224
4.
Quantile regressions with dependent errors
229
5.
Nonparametric and semiparametric QR models
231
6.
Other dynamic quantile models
237
7.
Extremal quantile regressions
240
8.
Quantile regression for nonstationary time series
242
9.
Time series quantile regression applications
247
10.
Conclusion
255
Acknowledgment
255
References
255
Part V. Biostatistical Applications
259
Ch.
10.
Frequency Domain Techniques in the Analysis
of
DNA
Sequences
261
David S.
Stoffer
1.
Introduction
261
2.
The spectral envelope
267
viii Table of
Contents
3.
Local spectral
envelope
274
4.
Detection of genomic differences
283
Appendix: Principal component and canonical correlation analysis
for time series
289
Acknowledgment
293
References
293
Ch.
11.
Spatial Time Series Modeling for fMRI Data Analysis
in Neurosciences
297
Tohru Ozaki
1.
Introduction
297
2.
A traditional approach: Spatial and temporal covariance functions
298
3.
SPM and the implied determinism
299
4.
Innovation approach and the NN-ARX model
302
5.
Likelihood and the significance of the assumptions
304
6.
Applications to connectivity study and brain mapping
310
7.
Concluding remarks
311
Acknowledgement
312
References
312
Ch.
12.
Count Time Series Models
315
Konstantinos Fokianos
1.
Introduction
315
2.
Poisson
regression modeling
317
3.
Poisson
regression models for count time series
319
4.
Other regression models for count time series
334
5.
Integer
autoregressive
models
337
6.
Conclusions
343
Appendix
343
Acknowledgments
344
References
344
Part VI. Nonstationary Time Series
349
Ch.
13.
Locally Stationary Processes
351
Rainer
Dahinaus
1.
Introduction
351
2.
Time varying
autoregressive
processes-A deep example
353
3.
Local likelihoods, derivative processes, and nonlinear models with time
varying parameters
367
4.
A general definition, linear processes and time varying spectral densities
379
Table
of Contents
5.
Gaussian likelihood theory for locally stationary processes
387
6.
Empirical spectral processes
393
7.
Additional topics and further references
402
Acknowledgment
408
References
408
Ch.
14.
Analysis of Multivariate Nonstationary Time Series Using
the Localized Fourier Library
415
Hernando Ombao
1.
Introduction
415
2.
Overview of SLEX analysis
419
3.
Selecting the best SLEX signal representation
424
4.
Classification and discrimination of time series
432
5.
Summary
442
Acknowledgments
442
References
442
Ch.
15.
An Alternative Perspective on Stochastic Coefficient Regression
Models
445
Suhasini
Subba
Rao
1.
Introduction
446
2.
The stochastic coefficient regression model
447
3.
The estimators
450
4.
Testing for randomness of the coefficients in the SCR model
453
5.
Asymptotic properties of the estimators
456
6.
Real data analysis
465
Acknowledgments
473
References
473
Part
VII.
Spatio-Temporal
Time Series
475
Ch.
16.
Hierarchical Bayesian Models for Space-Time Air Pollution
Data
477
Suj
it K. Sahu
1.
Introduction
477
2.
Hierarchical models
479
3.
Prediction details
484
4.
An example
485
5.
Further discussion
492
Acknowledgment
492
Appendix: Conditional distributions for Gibbs sampling
492
References
494
χ
Table of Contents
Ch.
17.
Karhunen-Loéve
Expansion of Temporal
and
Spatio-Temporal
Processes
497
Lara
Fontanella
and
Luigi Ippoliti
1.
Introduction
497
2.
Karhunen-Loéve
expansion of one-dimensional processes
498
3.
Multiresolution
Karhunen-Loéve
505
4.
Karhunen-Loéve
expansion of coupled one-dimensional processes
510
5.
Karhunen-Loéve
expansion of spatio-temporal processes
513
6.
Discussion
517
Acknowledgments
518
References
518
Ch.
18.
Statistical Analysis of
Spatio-Temporal
Models
and Their Applications
521
T. Subba
Rao and Gy. Terdik
1.
Introduction and basic ideas
522
2.
Measures for linear dependence and linearity of stationary spatial process
527
3.
Models for spatial processes defined on lattices
529
4.
Frequency domain approach for the estimation of CAR models
530
5.
Spatio-temporal processes
531
6.
Multivariate
AR
and STAR models
534
Concluding Remarks
538
Acknowledgements
539
References
539
Part
VIII.
Continuous Time Series
541
Ch.
19.
Levy-Driven Time Series Models for Financial Data
543
Peter
Brockwell
and Alexander Lindner
1.
Introduction
543
2.
Levy processes
544
3.
Lévy-driven
CARMA(p,<7) processes
545
4.
A continuous-time stochastic volatility model
549
5.
Integrated CARMA processes and spot volatility modeling
550
6.
Generalized Ornstein-Uhlenbeck processes
555
7.
Continuous-time GARCH processes
558
Acknowledgments
561
References
561
Ch.
20.
Discrete and Continuous Time Extremes of Stationary
Processes
565
K.F. Turkman
1.
Introduction
565
2.
Conditions and main results
571
Table
of Contents
3.
Periodogram
578
Acknowledgment
581
References
581
Part IX. Spectral and Wavelet Methods
583
Ch.
21.
The Estimation of Frequency
585
Barry G. Quinn
1.
Introduction
585
2.
Basic model
586
3.
Properties of the periodogram maximizer
590
4.
Links with
ARMA
processes
591
5.
Autoregressive
approximation
592
6.
Pisarenko s technique
596
7.
MUSIC
599
8.
An efficient technique based on
ARMA
filtering
600
9.
Maximizing the periodogram: practicalities
604
10.
Discrete Fourier transform-based methods
606
11.
Estimation using only the moduli of the DFT
608
12.
More than one sinusoid
610
13.
Complex sinusoids
618
14.
Related problems and areas
619
References
619
Ch.
22.
A Wavelet Variance Primer
623
Donald B. Percival and Debashis
Mondai
1.
Introduction
623
2.
Maximal overlap discrete wavelet transform
625
3.
Analysis of variance via the MODWT
628
4.
Definition and basic properties of wavelet variance
630
5.
Basic estimators of the wavelet variance
632
6.
Specialized estimators of the wavelet variance
637
7.
Combining wavelet variance estimators across scales
641
8.
Examples
645
9.
Concluding remarks
653
Acknowledgments
654
References
654
Part X. Computational Methods
659
Ch.
23.
Time Series Analysis with
R
661
A. Ian McLeod, Hao Yu and
Esani Mahdi
1.
Time series plots
663
2.
Base packages: stats and
datasets
668
Table
of Contents
3.
More linear time series analysis
671
4.
Time series regression
677
5.
Nonlinear time series models
683
6.
Unit-root tests
685
7.
Cointegration
and
VAR
models
693
8.
GARCH time series
694
9.
Wavelet methods in time series analysis
696
10.
Stochastic differential equations (SDEs)
699
11.
Conclusion
700
Acknowledgments
702
A. Appendix
702
References
707
Index
713
Handbook of Statistics: Contents of Previous Volumes
727
|
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| language | English |
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| spelling | Time series analysis methods and applications ed. by Tata Subba Rao ... 1. ed. Amsterdam [u.a.] Elsevier 2012 XVIII, 755 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics 30 Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Subba Rao, T. Sonstige (DE-588)1023590727 oth Handbook of statistics 30 (DE-604)BV000002510 30 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025085682&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Time series analysis methods and applications Handbook of statistics Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4143413-4 |
| title | Time series analysis methods and applications |
| title_auth | Time series analysis methods and applications |
| title_exact_search | Time series analysis methods and applications |
| title_full | Time series analysis methods and applications ed. by Tata Subba Rao ... |
| title_fullStr | Time series analysis methods and applications ed. by Tata Subba Rao ... |
| title_full_unstemmed | Time series analysis methods and applications ed. by Tata Subba Rao ... |
| title_short | Time series analysis |
| title_sort | time series analysis methods and applications |
| title_sub | methods and applications |
| topic | Time series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Time series analysis Zeitreihenanalyse Aufsatzsammlung |
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