出版社: Springer
副标题: Third edition
出版年: 2010-11-1
页数: 610
定价: USD 99.00
装帧: Paperback
ISBN: 9781441978646
内容简介 · · · · · ·
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 non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. The book is designed to be ...
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 non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or 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 methodology are separated to allow presentations on different levels. Material from the earlier 1988 Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models. The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware. Robert H. Shumway is Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a member of the Inernational Statistical Institute. He won the 1986 American Statistical Association Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center Statistics Award; both awards were for joint papers on time series applications. He is the author of a previous 1988 Prentice-Hall text on applied time series analysis and is currenlty a Departmental Editor for the Journal of Forecasting. David S. Stoffer is Professor of Statistics at the University of Pittsburgh. He has made seminal contributions to the analysis of categorical time series and won the 1989 American Statistical Association Award for Outstanding Statistical Application in a joint paper analyzing categorical time series arising in infant sleep-state cycling. He is currently an Associate Editor of the Journal of Forecasting and has served as an Associate Editor for the Journal fo the American Statistical Association. --This text refers to an alternate Hardcover edition.
目录 · · · · · ·
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
· · · · · · (更多)
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
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
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
4 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
5 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 GARCH 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 ARMAX Models 301
6 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
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
Appendix A: Large Sample Theory 507
A.1 Convergence Modes 507
A.2 Central Limit Theorems 515
A.3 The Mean and Autocorrelation Functions 518
Appendix B: Time Domain Theory 527
B.1 Hilbert 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.1 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.1 First Things First 559
R.1.1 Included Data Sets 560
R.1.2 Included Scripts 562
R.2 Getting Started 567
R.3 Time Series Primer 571
· · · · · · (收起)
原文摘录 · · · · · ·
喜欢读"Time Series Analysis and Its Applications"的人也喜欢的电子书 · · · · · ·
喜欢读"Time Series Analysis and Its Applications"的人也喜欢 · · · · · ·
Time Series Analysis and Its Applications的书评 · · · · · · ( 全部 3 条 )
> 更多书评 3篇
这本书的其他版本 · · · · · · ( 全部6 )
-
Springer (2017)暂无评分 6人读过
-
世界图书出版公司 (2009)暂无评分 7人读过
-
Springer (2006)8.6分 19人读过
-
机械工业出版社 (2020)暂无评分
以下书单推荐 · · · · · · ( 全部 )
- R (C6H5NO2)
- you R ready (阿道克)
- 机器学习-数学理论与实际领域应用入门进阶 (xiaoliable)
- stat (蓝莓泡芙)
- 数据铺子 (阿道克)
谁读这本书? · · · · · ·
二手市场
· · · · · ·
- 在豆瓣转让 有403人想读,手里有一本闲着?
订阅关于Time Series Analysis and Its Applications的评论:
feed: rss 2.0
0 有用 BreeZe 2016-05-02 11:07:55
只看了前面基础几章
0 有用 Sofie 2016-05-26 14:44:42
8.1 Time Series Analysis and Its Applications - Shumway and Stoffer (Springer, 2011)
0 有用 Iris 2017-02-03 03:21:14
一本书都是公式,讲的也不错,但是入门来说不如Tsay那本。
1 有用 羊肉烤黄皮子 2016-02-17 13:07:33
也算读过(呗)
0 有用 灰太狼 2014-11-23 18:02:40
由auto和cross correlation起到regression,逻辑很简单很顺畅。看了第1,3章感觉已经足够了。缺点是车轱辘话太多,严重拖慢阅读速度。亮点是时序相关的R examples,系统详实。
1 有用 白马啸西风 2022-04-10 21:48:11
@2021-01-05 20:32:37
1 有用 锦鳞弄波 2020-05-09 02:58:10
令人头秃 立flag:一定会做题的
5 有用 芳煙 2020-04-26 17:00:44
今天整理文件忽然看到这本书的PDF,之前学Macroeconometric主要参考的就是这本和Hamilton的那本,不过个人更喜欢后者,只是当时因为Hamilton的那本版本太老又没有R的代码演示才看的这本,不过这本框架很棒,循序渐进,最后几章难度跨越有点大。打四星是因为这是我唯一只得了A的课。
0 有用 Chen Zhou 2020-01-09 22:49:06
时间序列的经典实用课本,上手快,有许多实用例子;理论的部分写的比较清楚,内容cover也比较全面。
0 有用 NOVS 2019-04-20 08:56:30
补标记。个人感觉废话太多了,不够精炼。