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Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data

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  • R. H. Glendinning

    (Defence Research Agency)

Abstract

Summary We show how a number of commonly used estimates of the inverse autocorrelation function can be modified to deal with outlier contaminated data. The robust analogues of the orthogonal and interpolation based techniques appear to be new, and provide an alternative to the robust autoregressive approach. We examine the performance of these techniques in a large scale numerical experiment. This shows significant improvements in performance in outlier contaminated data when robust techniques are used. While there was no uniformly best robust technique, our experiments support the use of the autoregressive approach to avoid catastrophic reductions in performance, and robust interpolation for short series corrupted by few outliers.

Suggested Citation

  • R. H. Glendinning, 2000. "Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data," Computational Statistics, Springer, vol. 15(4), pages 541-565, December.
    • Handle: RePEc:spr:compst:v:15:y:2000:i:4:d:10.1007_s001800000048
    DOI: 10.1007/s001800000048
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    References listed on IDEAS

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    1. G. Masarotto, 1987. "Robust Identification of Autoregressive Moving Average Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 214-220, June.
    2. Ledolter, Johannes, 1989. "The effect of additive outliers on the forecasts from ARIMA models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 231-240.
    3. Hector Allende & Siegfried Heiler, 1992. "Recursive Generalized M Estimates For Autoregressive Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(1), pages 1-18, January.
    4. R. J. Bhansali, 1983. "A Simulation Study of Autoregressive and Window Estimators of the Inverse Correlation Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 141-149, June.
    5. W. Schmid, 1996. "An Outlier Test For Time Series Based On A Two‐Sided Predictor," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(5), pages 497-510, September.
    6. Francesco Battaglia, 1988. "On The Estimation Of The Inverse Correlation Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(1), pages 1-10, January.
    7. Antti J. Kanto, 1987. "A Formula For The Inverse Autocorrelation Function Of An Autoregressive Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(3), pages 311-312, May.
    8. T. Subba Rao & M. M. Gabr, 1989. "The Estimation Of Spectrum, Inverse Spectrum And Inverse Autocovariances Of A Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(2), pages 183-202, March.
    9. Gwo‐Hsing Yu & Yow‐Chang Lin, 1991. "A Methodology For Selecting Subset Autoregressive Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 363-373, July.
    10. R. J. Bhansali, 1983. "Estimation Of The Order Of A Moving Average Model From Autoregressive And Window Estimates Of The Inverse Correlation Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(3), pages 137-162, May.
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