Multiple Time Series
Published on 4. November 2009
536 pages
978-0-470-31713-6 (ISBN)
Description
The Wiley Series in Probability and Statistics is a collection of topics of current research interests in both pure and applied statistics and probability developments in the field and classical methods. This series provides essential and invaluable reading for all statisticians, whether in academia, industry, government, or research.
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E. J. Hannan
Multiple Time Series
Book
09/1990
Wiley
€114.52
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Edward James Hannan FAA FASSA was an Australian statistician who is the co-discoverer of the Hannan-Quinn information criterion. He studied at the University of Melbourne and completed a PhD at the Australian National University under the supervision of Patrick A. P. Moran.
Content
- Multiple Time Series
- Contents
- PART I. BASIC THEORY
- CHAPTER I. INTRODUCTORY THEORY
- 1. Introduction
- 2. Differentiation and Integration of Stochastic Processes
- 3. Some Special Models
- 4. Stationary Processes and their Covariance Structure
- 5. Higher Moments
- 6. Generalized Random Processes
- EXERCISES
- APPENDIX
- CHAPTER II. THE SPECTRAL THEORY OF VECTOR PROCESSES
- 1. Introduction
- 2. The Spectral Theorems for Continuous-Time Stationary Processes
- 3. Sampling a Continuous-Time Process. Discrete Time Processes
- 4. Linear Filters
- 5 . Some Special Models
- 6. Some Spectral Theory for Nonstationary Processes
- 7. Nonlinear Transformations of Random Processes
- 8. Higher Order Spectra
- 9. Spectral Theory for GRP
- 10. Spectral Theories for Homogeneous Random Processes on Other Spaces
- 11. Filters, General Theory
- EXERCISES
- APPENDIX
- CHAPTER III. PREDICTION THEORY AND SMOOTHING
- 1. Introduction
- 2. Vector Discrete-Time Prediction for Rational Spectra
- 3. The General Theory for Stationary, Discrete-Time, Scalar Processes
- 4. The General Theory for Stationary, Continuous-Time, Scalar Processes
- 5. Vector Discrete-Time Prediction
- 6. Problems of Interpolation
- 7. Smoothing and Signal Measurement
- 8. Kalman Filtering
- 9. Smoothing Filters
- EXERCISES
- PART II. INFERENCE
- CHAPTER IV. THE LAWS OF LARGE NUMBERS AND THE CENTRAL LIMIT THEOREM
- 1. Introduction
- 2. Strictly Stationary Processes. Ergodic Theory
- 3. Second-Order Stationary Processes. Ergodic Theory
- 4. The Central Limit Theorem
- EXERCISES
- APPENDIX
- CHAPTER V. INFERENCE ABOUT SPECTRA
- 1. Introduction
- 2. The Finite Fourier Transform
- 3. Alternative Computational Procedures for the FFT
- 4. Estimates of Spectral for large Nand N/M
- 5. The Asymptotic Distribution of Spectral Estimates
- 6. Complex Multivariate Analysis
- EXERCISES
- APPENDIX
- CHAPTER VI. INFERENCE FOR RATIONAL SPECTRA
- 1. Introduction
- 2. Inference for Autoregressive Models. Asymptotic Theory
- 3. Inference for Autoregressive Models. Some Exact Theory
- 4. Moving Average and Mixed Autoregressive, Moving Average Models. Introduction
- 5. The Estimation of Moving Average and Mixed Moving Average Autoregressive Models Using Spectral Methods
- 6. General Theories of Estimation for Finite Parameter Models
- 7. Tests of Goodness of Fit
- 8. Continuous-Time Processes and Discrete Approximations
- EXERCISES
- APPENDIX
- CHAPTER VII. REGRESSION METHODS
- 1. Introduction
- 2. The Efficiency of Least Squares. Fixed Sample Size
- 3. The Efficiency of Least Squares. Asymptotic Theory
- 4. The Efficient Estimation of Regressions
- 5. The Effects of Regression Procedures on Analysis of Residuals
- 6. Tests for Periodicities
- 7. Distributed Lag Relationships
- EXERCISES
- APPENDIX
- MATHEMATICAL APPENDIX
- BIBLIOGRAPHY
- TABLE OF NOTATIONS
- INDEX
