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Optimal linear interpolation of multiple missing values

Author

Listed:
  • Tucker S. McElroy

    (U.S. Census Bureau)

  • Dimitris N. Politis

    (University of California, San Diego)

Abstract

The problem of linear interpolation in the context of a multivariate time series having multiple (possibly non-consecutive) missing values is studied. A concise formula for the optimal interpolating filter is derived, and illustrations using two simple models are provided.

Suggested Citation

  • Tucker S. McElroy & Dimitris N. Politis, 2022. "Optimal linear interpolation of multiple missing values," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 471-483, October.
    • Handle: RePEc:spr:sistpr:v:25:y:2022:i:3:d:10.1007_s11203-022-09269-5
    DOI: 10.1007/s11203-022-09269-5
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    References listed on IDEAS

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    1. Cheng, R. & Pourahmadi, M., 1997. "Prediction with incomplete past and interpolation of missing values," Statistics & Probability Letters, Elsevier, vol. 33(4), pages 341-346, May.
    2. Inoue, Akihiko, 2020. "Closed-form expression for finite predictor coefficients of multivariate ARMA processes," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    3. Mohsen Pourahmadi, 1989. "Estimation And Interpolation Of Missing Values Of A Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(2), pages 149-169, March.
    4. Pascal Bondon, 2005. "Influence of Missing Values on the Prediction of a Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 519-525, July.
    5. Jurgen Franke, 1984. "On The Robust Prediction And Interpolation Of Time Series In The Presence Of Correlated Noise," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(4), pages 227-244, July.
    Full references (including those not matched with items on IDEAS)

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