On asymptotic normality of sequential LS-estimate for unstable autoregressive process AR(2)

Leonid Galtchouk, Victor Konev

    Результат исследований: Материалы для журналаСтатьярецензирование

    4 Цитирования (Scopus)

    Аннотация

    For estimating parameters in an unstable AR(2) model, the paper proposes a sequential least squares estimate with a special stopping time defined by the trace of the observed Fisher information matrix. It is shown that the sequential LSE is asymptotically normally distributed in the stability region and on its boundary in contrast to the usual LSE, having six different types of asymptotic distributions on the boundary depending on the values of the unknown parameters. The asymptotic behavior of the stopping time is studied.

    Язык оригиналаАнглийский
    Страницы (с-по)2616-2636
    Число страниц21
    ЖурналJournal of Multivariate Analysis
    Том101
    Номер выпуска10
    DOI
    СостояниеОпубликовано - ноя 2010

    ASJC Scopus subject areas

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

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