LEADER 05397nam 2200673Ia 450 001 9910144692703321 005 20170816115045.0 010 $a1-282-30767-3 010 $a9786612307676 010 $a0-470-31691-8 010 $a0-470-31775-2 035 $a(CKB)1000000000687568 035 $a(EBL)469406 035 $a(OCoLC)460049697 035 $a(SSID)ssj0000339295 035 $a(PQKBManifestationID)11243116 035 $a(PQKBTitleCode)TC0000339295 035 $a(PQKBWorkID)10324128 035 $a(PQKB)11277626 035 $a(MiAaPQ)EBC469406 035 $a(PPN)159065062 035 $a(EXLCZ)991000000000687568 100 $a19950329d1996 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to statistical time series$b[electronic resource] /$fWayne A. Fuller 205 $a2nd ed. 210 $aNew York $cJ. Wiley$dc1996 215 $a1 online resource (734 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-55239-9 320 $aIncludes bibliographical references and index. 327 $aIntroduction to Statistical Time Series; Contents; Preface to the First Edition; Preface to the Seeond Edition; List of Principal Results; List of Examples; 1. Introduction; 1.1 Probability Spaces; 1.2 Time Series; 1.3 Examples of Stochastic Processes; 1.4 Properties of the Autocovariance and Autocorrelation Functions; 1.5 Complex Valued Time Series; 1.6 Periodic Functions and Periodic Time Series; 1.7 Vector Valued Time Series; References; Exercises; 2. Moving Average and Autoregressive Processes; 2.1 Moving Average Processes; 2.2 Absolutely Summable Sequences and Infinite Moving Averages 327 $a2.3 An Introduction to Autoregressive Time Series2.4 Difference Equations; 2.5 The Second Order Autoregressive Time Series; 2.6 Alternative Representations of Autoregressive and Moving Average Processes; 2.7 Autoregressive Moving Average Time Series; 2.8 Vector Processes; 2.9 Prediction; 2.10 The Wold Decomposition; 2.11 Long Memory Processes; References; Exercises; 3. Introduction to Fourier Analysis; 3.1 Systems of Orthogonal Functions-Fourier Coefficients; 3.2 Complex Representation of Trigonometric Series; 3.3 Fourier Transform-Functions Defined on the Real Line 327 $a3.4 Fourier Transform of a ConvolutionReferences; Exercises; 4. Spectral Theory and Wtering; 4.1 The Spectrum; 4.2 Circulants-Diagonalization of the Covariance Matrix of Stationary Process; 4.3 The Spectral Density of Moving Average and Autoregressive Time Series; 4.4 Vector Processes; 4.5 Measurement Error-Signal Detection; 4.6 State Space Models and Kalman Filtering; References; Exercises; 5. Some Large Sample Theory; 5.1 Order in Probability; 5.2 Convergence in Distribution; 5.3 Central Limit ""heorems; 5.4 Approximating a Sequence of Expectations; 5.5 Estimation for Nonlinear Models 327 $a5.5.1 Estimators that Minimize an Objective Function5.5.2 One-Step Estimation; 5.6 Instrumental Variables; 5.7 Estimated Generalized Least Squares; 5.8 Sequences of Roots of Polynomials; References; Exercises; 6. Estimation of the Mean and Autoeorrelations; 6.1 Estimation of the Mean; 6.2 Estimators of the Autocovariance and Autoconelation Functions; 6.3 Central Limit Theorems for Stationary Time Series; 6.4 Estimation of the Cross Covariances; References; Exercises; 7. The Periodogram, Estimated Spectrum; 7.1 The Periodogram; 7.2 Smoothing, Estimating the Spectrum 327 $a7.3 Other Estimators of the Spectrum7.4 Multivariate Spectral Estimates; References; Exercises; 8. Parameter Wmation; 8.1 First Order Autoregressive Time Series; 8.2 Higher Order Autoregressive Time Series; 8.2.1 Least Squares Estimation for Univariate Processes; 8.2.2 Alternative Estimators for Autoregressive Time Series; 8.2.3 Multivariate Autoregressive Time Series; 8.3 Moving Average Time Series; 8.4 Autoregressive Moving Average Time Series; 8.5 Prediction with Estimated Parameters; 8.6 Nonlinear Processes; 8.7 Missing and Outlier Observations; 8.8 Long Memory Processes; References 327 $aExercises 330 $aThe subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and th 410 0$aWiley series in probability and statistics.$pProbability and statistics. 606 $aRegression analysis 606 $aTime-series analysis 615 0$aRegression analysis. 615 0$aTime-series analysis. 676 $a519.232 676 $a519.5 676 $a519.55 700 $aFuller$b Wayne A$0116991 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144692703321 996 $aIntroduction to statistical time series$9196710 997 $aUNINA