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Robust parameter estimation for stationary processes by an exotic disparity from prediction problem

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  • Liu, Yan

Abstract

A new class of disparities from the point of view of prediction problem is proposed for minimum contrast estimation of spectral densities of stationary processes. We investigate asymptotic properties of the minimum contrast estimators based on the new disparities for stationary processes with both finite and infinite variance innovations. The relative efficiency and the robustness against randomly missing observations are shown in our numerical simulations.

Suggested Citation

  • Liu, Yan, 2017. "Robust parameter estimation for stationary processes by an exotic disparity from prediction problem," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 120-130.
    • Handle: RePEc:eee:stapro:v:129:y:2017:i:c:p:120-130
    DOI: 10.1016/j.spl.2017.05.005
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    References listed on IDEAS

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    1. Fujisawa, Hironori & Eguchi, Shinto, 2008. "Robust parameter estimation with a small bias against heavy contamination," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 2053-2081, October.
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    Cited by:

    1. Kun Chen & Rui Huang, 2021. "Robust empirical likelihood for time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 4-18, January.
    2. Yuri A. Dubnov & Alexandr V. Boulytchev, 2023. "Accelerated Maximum Entropy Method for Time Series Models Estimation," Mathematics, MDPI, vol. 11(18), pages 1-15, September.

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