Skip to main content
SpringerLink
Log in

Non-Gaussian Time Series Models

  • Conference paper
Book cover Time Series Analysis and Applications to Geophysical Systems

Part of the book series: The IMA Volumes in Mathematics and its Applications ((IMA,volume 45))

  • 370 Accesses

Abstract

Non-Gaussian linear time series models are discussed. The ways in which they differ from Gaussian models are noted. This is particularly the case for prediction and parameter or transfer function estimation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aki K., Quantitative Seismology Theory and Methods, Vol. 2, W. Freeman and Co., 1980.

    Google Scholar 

  2. Breidt F.J., Davis R., and Trindade A., Least absolute deviation estimation for all-pass time series, Ann. Stat. 29 (2001), 919–946.

    MathSciNet  MATH  Google Scholar 

  3. Breidt F.J., Davis R.A., Lii K.S., and Rosenblatt M., Maximum likelihood estimation for noncausal autoregressive processes, J. Multivar. Anal. 36 (1991), 175–198.

    Article  MathSciNet  MATH  Google Scholar 

  4. Brockwell P. and Davis R., Time Series: Theory and Methods, 2nd edition, Springer, 1991.

    Google Scholar 

  5. Cheng Q., On the unique representation of non-Gaussian linear processes, Ann. Stat. 20 (1992), 1143–1145.

    Article  MATH  Google Scholar 

  6. Donoho D., On minimum entropy deconvolution in Applied Time Series Analysis (D. Findley, ed.), 1981, pp. 565–608.

    Google Scholar 

  7. Gassiat E., Estimation semi-param étrique d’un modèle autorégressif stationnaire multi-indice non nécessairement causal, Ann. Inst. H. Poincaré Probab. Statist. 26 (1990), 181–205.

    MathSciNet  MATH  Google Scholar 

  8. Gassiat E., Adaptive estimation in noncausal stationary AR processes, Ann. Stat. 21 (1993), 2022–2042.

    Article  MathSciNet  MATH  Google Scholar 

  9. Kanter M., Lower bounds for nonlinear prediction error in moving average processes, Ann. Prob. 7 (1979), 128–138.

    Article  MathSciNet  MATH  Google Scholar 

  10. Kreiss J., On adaptive estimation in autoregressive models when there are nuisance functions, Stat. and Decisions 5 (1987), 59–76.

    MathSciNet  MATH  Google Scholar 

  11. Lii K.S. and Rosenblatt M., Deconvolution and estimation of transfer function phase and coefficients for non-Gaussian linear processes, Ann. Stat. 10 (1982), 1195–1208.

    Article  MathSciNet  MATH  Google Scholar 

  12. Lii K.S. and Rosenblatt M., Maximum likelihood estimation of non-Gaussian nonminimum phase ARMA sequences, Stat. Sin. 6 (1996), 1–22.

    MathSciNet  MATH  Google Scholar 

  13. Robinson E.A., Time Series Analysis and Applications, Goose Pond Press, 1981.

    MATH  Google Scholar 

  14. Rosenblatt M., Gaussian and Non-Gaussian Linear Time Series and Random Fields, Springer, 2000.

    Book  MATH  Google Scholar 

  15. Wiggins R.A., Minimum entropy deconvolution, Geoexploration16 (1978), 21–35.

    Article  Google Scholar 

  16. Yaglom A.M., Correlation Theory of Stationary and Related Random Functions, Vols. 1, 2, Springer, 1986.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of California, San Diego, La Jolla, CA, 92093-0112, USA

    Murray Rosenblatt

Authors
  1. Murray Rosenblatt
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Statistics, University of California, Berkeley, Evens Hall, 367, 94720-3860, Berkeley, CA, USA

    David R. Brillinger

  2. Department of Earth and Environmental Engineering Henry Krumb School of Mines, Columbia University, Seely Mudd Building 500 120th Street, 918, 10027, New York, NY, USA

    Enders Anthony Robinson

  3. Department of Statistics, University of California, Los Angeles, Math-Science Building, 8142, 90095-1554, Los Angeles, CA, USA

    Frederic Paik Schoenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag New York, LLC

About this paper

Cite this paper

Rosenblatt, M. (2004). Non-Gaussian Time Series Models. In: Brillinger, D.R., Robinson, E.A., Schoenberg, F.P. (eds) Time Series Analysis and Applications to Geophysical Systems. The IMA Volumes in Mathematics and its Applications, vol 45. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2962-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-2962-9_12

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7735-4

  • Online ISBN: 978-1-4612-2962-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation