Hardbound. In this volume prominent workers in the field discuss various time series methods in the time domain. The topics included are autoregressive-moving average models, control, estimation, identification, model selection, non-linear time series, non-stationary time series, prediction, robustness, sampling designs, signal attenuation, and speech recognition. This volume complements Handbook of Statistics 3: Time Series in the Frequency Domain.
Author(s): P. R. Krishnaiah
Series: Handbook of Statistics
Edition: 1
Publisher: Elsevier Science
Year: 2005
Language: English
Pages: 483
Handbook of Statistics 5: Time Series in the Time Domain......Page 1
Preface......Page 2
Contributors......Page 4
1. Nonstationary Autoregressive Time Series......Page 6
2. Non-Linear Time Series Models and Dynamical Systems......Page 29
3. Autoregressive Moving Average Models, Intervention Problems and Outlier Detection in Time Series......Page 86
4. Robustness in Time Series and Estimating ARMA Models......Page 120
5. Time Series Analysis with Unequally Spaced Data......Page 157
6. Various Model Selection Techniques in Time Series Analysis......Page 178
7. Estimation of Parameters in Dynamical Systems......Page 187
8. Recursive Identification, Estimation and Control......Page 210
9. General Structure and Parametrization of ARMA and State-Space Systems and its Relation to Statistical Problems......Page 253
10. Harmonizable, Cramer, and Karhunen Classes of Processes......Page 274
11. On Non-Stationary Time Series......Page 306
12. Harmonizable Filtering and Sampling of Time Series......Page 316
13. Sampling Designs for Time Series......Page 332
14. Measuring Attenuation......Page 358
15. Speech Recognition Using LPC Distance Measures......Page 382
16. Varying Coefficient Regression......Page 407
17. Small Samples and Large Equation Systems......Page 444
Subject Index......Page 473
Handbook of Statistics: Contents of Previous Volumes......Page 478
