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Time Series Analysis from a Statistical Viewpoint

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Astronomical Time Series

Part of the book series: Astrophysics and Space Science Library ((ASSL,volume 218))

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Abstract

Time series analysis is an enormous field of study in mathematical statistics, econometrics, engineering signal processing, and other fields. This paper gives a brief and incomplete outline of some important themes in this literature that may be helpful in the investigation of variable astronomical phenomena. Autoregressive (ARMA) models can be highly effective for aperiodic but correlated time series as seen in accretion disk systems, active galactic nuclei, and gamma-ray bursts. State space representations, or Kalman filtering, provide a powerful mathematical environment for study of nonstationary, nonlinear, quasi-periodic, unevenly spaced, and Poisson time series in astronomy.

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References

  • Akaike, H., 1973, in 2nd Intl. Syrup. in Information Theory ( B. N. Tetroc & F. Caski, eds.), Budapest: Akad. Kiado, p. 267

    Google Scholar 

  • Babu, G. J. & Feigelson, E. D., 1996, Astrostatistics, London: Chapman & Hall

    MATH  Google Scholar 

  • Babu, G. J. & Feigelson, E. D. (eds.), 1997, Statistical Challenges in Modern Astronomy II, NY: Springer

    MATH  Google Scholar 

  • Bloomfield, R., 1976, Fourier Analysis of Time Series: An Introduction, NY: Wiley

    MATH  Google Scholar 

  • Box, G. E., Jenkins, G. M. & Reinsel, G. C., 1994, Time Series Analysis: Forecasting and Control, Englewood Cliffs NJ: Prentice-Hall

    MATH  Google Scholar 

  • Brillinger, D. R. & Krishnaiah, P. R. (eds.), 1983, Time Series in the Frequency Domain (Handbook of Statistics vol. 3 ), Amsterdam: North- Holland

    Google Scholar 

  • Chatfield, C., 1989, The Analysis of Time Series: An Introduction, 4th ed., London: Chapman & Hall

    MATH  Google Scholar 

  • Chui, C. K., 1992, Wavelets: A Tutorial in Theory and Applications, Boston: Academic

    MATH  Google Scholar 

  • Cohen, L., 1995, Time-Frequency Analysis, Englewood Cliffs NJ: Prentice Hall

    Google Scholar 

  • Cromwell, J. B., Hannan, M. J., Labys, W. C. & Terraza, M., 1994, Multivariate Tests for Time Series Models, Thousand Oaks CA: Sage

    Google Scholar 

  • Dutt, A. & Rohklin, V., 1993, SIAM J. Stat. Comput. 14, 1368

    Article  MATH  Google Scholar 

  • Enders, W., 1995, Applied Econometric Time Series, NY: Wiley

    Google Scholar 

  • Feigelson, E. D., 1997, in Applications of Time Series Analysis in Astronomy and Meteorology ( T. Subba Rao, M. B. Priestley & O. Lessi, eds.), London: Chapman & Hall

    Google Scholar 

  • Feigelson, E. D. & Babu, G. J. (eds.), 1992, Statistical Challenges in Modern Astronomy, NY: Springer

    Google Scholar 

  • Franke, J. & Seligmann, 1993, in Developments in Time Series Analysis (ed. T. Subba Rao ), London: Chapman & Hall, p. 310

    Google Scholar 

  • Fuller, W. A., 1996, Introduction to Statistical Time Series, 2nd ed., NY: Wiley

    MATH  Google Scholar 

  • Greene, W. H., 1993, Econometric Analysis, 2nd ed., NY: Macmillan.

    Google Scholar 

  • Hall, B. H., Cummins, C. & Schnake, R., 1991, Time Series Processor, Reference Manual, TSP Intl., P.O. Box 61015 Station A, Palo Alto CA 94306

    Google Scholar 

  • Hallin, M. & Puri, M. L., 1992, in New Directions in Time Series Analysis (vol. 1 ), NY: Springer

    Google Scholar 

  • Jones, R. H., in Applied time Series Analysis II (D. F. Findley, ed.), NY: Academin, p. 651

    Google Scholar 

  • Jones, R. H., 1984, in Time Series Analysis of Irregularly Observed Data ( E. Parzen, ed.), NY: Springer

    Google Scholar 

  • Kalman, R. E., 1960, Trans. Amer. Soc. lllech. Eng., J. Basic Engineering 82, 35

    Article  Google Scholar 

  • Kashiwagi, N. & Yanigimoto, T., 1992, Biometrics 48, 1187

    Article  Google Scholar 

  • Kitagawa, G. & Gersch, W., 1996, Smoothness Prior Analysis of Time Series, NY: Springer

    Book  Google Scholar 

  • Loredo, T. J., 1991, in Statistical Challenges in Modern Astronomy ( E. D. Feigelson & G. J. Babu, Eds.), NY: Springer, p. 275

    Google Scholar 

  • Meyer, Y., 1993, Wavelets, Algorithms and Applications, Philadelphia: SIAM

    MATH  Google Scholar 

  • Murtagh, F., Aussem, A. & Sarazin, M., 1995, Publ. Astr. Soc. Pacafic 107, 702

    Article  Google Scholar 

  • Newlove, M., Grether, D. M. & Carvalho, J. L., 1995, Analysis of Economic Time Series: A Synthesis, San Diego: Academic

    Google Scholar 

  • Nikias, C. L. & Petropulu, A. P., 1993, Higher-Order Spectra Analysis, Englewood Cliffs NJ: Prentice Hall

    MATH  Google Scholar 

  • Parzen, E. (ed.), Time Series Analysis of Irregularly Observed Data, NY: Springer, 1984

    MATH  Google Scholar 

  • Priestley, M. B., 1981, Spectral Analysis and Time Series, 2. vols., London: Academic

    MATH  Google Scholar 

  • Pole, A., West, M. and Harrison, J., 1994, Applied Bayesian Forecasting and Time Series Analysis, NY: Chapman & Hall

    MATH  Google Scholar 

  • Press,, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P., 1992, Numerical Recipes: The Art of Scientific Computing, 2nd ed., Cambridge: Cambridge University

    Google Scholar 

  • Priestley, M. B., 1988, Nonlinear and Non-Stationary Time Series Analysis, London: Academic

    Google Scholar 

  • Qian, S. & Chen, D., 1996, Joint Time-Frequency Analysis: Methods and Applications, Englewood Cliffs NJ: Prentice Hall

    Google Scholar 

  • Reilly, D. P., 1984, AUTOBOX, Complete Box-Jenkins Modeling,Automatic Forecasting Systems, Inc., P.O. Box 563, Hartboro PA USA

    Google Scholar 

  • SAS/ETS Users’ Guide, 1993, SAS Institute, SAS Campus Drive, Cary NC 27513 USA

    Google Scholar 

  • Scargle, J. D., 1984, in Statistical Challenges in Modern Astronomy ( E. Feigelson and G. J. Babu, eds.), NY: Springer, p. 411

    Google Scholar 

  • Scargle, J., Steiman-Cameron, Young, K., Donoho, D., Crutchfield, J. & Imamura, I., 1993, Astrophys. J. Lett. 411, L91.

    Article  Google Scholar 

  • Subba Rao, T. (ed.), 1997, Applications of Time Series Analysis in Astronomy and Meteorology, London: Chapman & Hall

    MATH  Google Scholar 

  • Vio, R., Cristiani, S., Lessi, O, & Provenzale, A., 1992, Astrophys. J. 391, 518

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Astronomy & Astrophysics, Pensylvania State University, University Park, PA, 16802, USA

    Eric D. Feigelson

Authors
  1. Eric D. Feigelson
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Editor information

Editors and Affiliations

  1. School of Physics and Astronomy, Tel-Aviv University, 69978, Tel-Aviv, Israel

    Dan Maoz , Amiel Sternberg  & Elia M. Leibowitz ,  & 

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© 1997 Springer Science+Business Media Dordrecht

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Feigelson, E.D. (1997). Time Series Analysis from a Statistical Viewpoint. In: Maoz, D., Sternberg, A., Leibowitz, E.M. (eds) Astronomical Time Series. Astrophysics and Space Science Library, vol 218. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8941-3_2

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  • DOI: https://doi.org/10.1007/978-94-015-8941-3_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4895-0

  • Online ISBN: 978-94-015-8941-3

  • eBook Packages: Springer Book Archive

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