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Introduction to time series modeling / Genshiro Kitagawa.

By: Material type: TextTextSeries: Monographs on statistics and applied probability ; 114.Publication details: Boca Raton : Chapman & Hall/CRC, 2010.Description: 307 p. : ill. ; [ca. 23-29] cmISBN:
  • 9781584889212
Subject(s): LOC classification:
  • QA402 .K55 2010
Available additional physical forms:
  • Also available as an electronic resource.
Contents:
1. Introduction and preparatory analysis -- 2. The covariance function -- 3. The power spectrum and the periodogram -- 4. Statistical modeling -- 5. The least squares method -- 6. Analysis of time series using ARMA models -- 7. Estimation of an AR model -- 8. The locally stationary AR model -- 9. Analysis of time series with a state-space model -- 10. Estimation of the ARMA model -- 11. Estimation of trends -- 12. The seasonal adjustment model -- 13. Time-varying coefficient AR model -- 14. Non-gaussian state-space model -- 15. The sequential Monte Carlo filter -- 16. Simulation -- A. Algorithms for nonlinear optimization -- B. Derivation of Levinson's algorithm -- C. Derivation of the Kalman filter and smoother algorithms -- D. Algorithm for the Monte Carlo filter.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata NBHM Collection 000SA.3 K62 (Browse shelf(Opens below)) Available 131927
Total holds: 0

Includes bibliographical references and index.

1. Introduction and preparatory analysis -- 2. The covariance function -- 3. The power spectrum and the periodogram -- 4. Statistical modeling -- 5. The least squares method -- 6. Analysis of time series using ARMA models -- 7. Estimation of an AR model -- 8. The locally stationary AR model -- 9. Analysis of time series with a state-space model -- 10. Estimation of the ARMA model -- 11. Estimation of trends -- 12. The seasonal adjustment model -- 13. Time-varying coefficient AR model -- 14. Non-gaussian state-space model -- 15. The sequential Monte Carlo filter -- 16. Simulation -- A. Algorithms for nonlinear optimization -- B. Derivation of Levinson's algorithm -- C. Derivation of the Kalman filter and smoother algorithms -- D. Algorithm for the Monte Carlo filter.

Also available as an electronic resource.

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