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

Yan Liu

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish
    Pages (from-to)120-130
    Number of pages11
    JournalStatistics and Probability Letters
    Volume129
    DOIs
    Publication statusPublished - 2017 Oct 1

    Fingerprint

    Robust Estimation
    Stationary Process
    Parameter Estimation
    Minimum Contrast Estimators
    Missing Observations
    Infinite Variance
    Prediction
    Relative Efficiency
    Spectral Density
    Asymptotic Properties
    Robustness
    Numerical Simulation
    Stationary process
    Parameter estimation
    Innovation
    Class
    Estimator
    Relative efficiency
    Spectral density
    Asymptotic properties

    Keywords

    • Asymptotic efficiency
    • Minimum contrast estimation
    • Prediction problem
    • Spectral density
    • Stationary process

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

    Cite this

    Robust parameter estimation for stationary processes by an exotic disparity from prediction problem. / Liu, Yan.

    In: Statistics and Probability Letters, Vol. 129, 01.10.2017, p. 120-130.

    Research output: Contribution to journalArticle

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