LEADER 04501nam 2200625Ia 450 001 9910144694603321 005 20170810195445.0 010 $a1-282-30760-6 010 $a9786612307607 010 $a0-470-31642-X 010 $a0-470-31713-2 035 $a(CKB)1000000000687554 035 $a(EBL)469488 035 $a(OCoLC)264615243 035 $a(SSID)ssj0000340643 035 $a(PQKBManifestationID)11253299 035 $a(PQKBTitleCode)TC0000340643 035 $a(PQKBWorkID)10408068 035 $a(PQKB)10893017 035 $a(MiAaPQ)EBC469488 035 $a(PPN)159306280 035 $a(EXLCZ)991000000000687554 100 $a19800319d1970 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple time series$b[electronic resource] /$fE. J. Hannan 210 $aNew York $cWiley$d1970 215 $a1 online resource (552 p.) 225 1 $aWiley series in probability and mathematical statistics 300 $aDescription based upon print version of record. 311 $a0-471-34805-8 320 $aIncludes bibliography: p. 519-527. 327 $aMultiple 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 327 $a6. Some Spectral Theory for Nonstationary Processes7. 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 327 $a6. Problems of Interpolation7. 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 327 $a5. The Asymptotic Distribution of Spectral Estimates6. 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 327 $a8. Continuous-Time Processes and Discrete ApproximationsEXERCISES; APPENDIX; CHAPTER VII. REGRESSION METHODS; 1. Introduction; 2. The Efficiency of Least Squares. Fixed Sample Size; 3. The Efficiency of Least Squares. Asymptotic Theory; 4. The Efficient Estimation of Regressions; 5. The Effects of Regression Procedures on Analysis of Residuals; 6. Tests for Periodicities; 7. Distributed Lag Relationships; EXERCISES; APPENDIX; MATHEMATICAL APPENDIX; BIBLIOGRAPHY; TABLE OF NOTATIONS; INDEX 410 0$aWiley series in probability and mathematical statistics. 606 $aMathematical statistics 606 $aTime-series analysis 615 0$aMathematical statistics. 615 0$aTime-series analysis. 676 $a519.232 676 $a519.8 700 $aHannan$b E. J$g(Edward James),$f1921-$021010 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144694603321 996 $aMultiple Time Series$9436679 997 $aUNINA