On uniform asymptotic normality of sequential least squares estimators for the parameters in a stable AR(p)

L. Galtchouk, V. Konev

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    For a stable autoregressive process of order p with unknown vector parameter θ, it is shown that under a sequential sampling scheme with the stopping time defined by the trace of the observed Fisher information matrix, the least-squares estimator of θ is asymptotically normally distributed uniformly in θ belonging to any compact set in the parameter region.

    Original languageEnglish
    Pages (from-to)119-142
    Number of pages24
    JournalJournal of Multivariate Analysis
    Volume91
    Issue number2
    DOIs
    Publication statusPublished - 1 Nov 2004

    Fingerprint

    Fisher information matrix
    Uniform Asymptotics
    Least Squares Estimator
    Asymptotic Normality
    Sampling
    Observed Information
    Sequential Sampling
    Fisher Information Matrix
    Stopping Time
    Stable Process
    Autoregressive Process
    Compact Set
    Trace
    Unknown
    Asymptotic normality
    Least squares estimator
    Stopping time
    Autoregressive process
    Fisher information

    Keywords

    • Autoregressive process
    • Least-squares estimator
    • Sequential estimation
    • Uniform asymptotic normality

    ASJC Scopus subject areas

    • Statistics and Probability
    • Numerical Analysis
    • Statistics, Probability and Uncertainty

    Cite this

    On uniform asymptotic normality of sequential least squares estimators for the parameters in a stable AR(p). / Galtchouk, L.; Konev, V.

    In: Journal of Multivariate Analysis, Vol. 91, No. 2, 01.11.2004, p. 119-142.

    Research output: Contribution to journalArticle

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