Approximate self-weighted LAD estimation of discretely observed ergodic ornstein-uhlenbeck processes

Hiroki Masuda

    研究成果: ジャーナルへの寄稿学術誌査読

    25 被引用数 (Scopus)


    We consider drift estimation of a discretely observed OrnsteinUhlenbeck process driven by a possibly heavy-tailed symmetric Lévy process with positive activity index β. Under an infill and large-time sampling design, we first establish an asymptotic normality of a self-weighted least absolute deviation estimator with the rate of convergence being √ nh1−1/βn, where n denotes sample size and hn > 0 the sampling mesh satisfying that hn → 0 and nhn → ∞. This implies that the rate of convergence is determined by the most active part of the driving Lévy process; the presence of a driving Wiener part leads to √ nhn, which is familiar in the context of asymptotically efficient estimation of diffusions with compound Poisson jumps, while a pure-jump driving Lévy process leads to a faster one. Also discussed is how to construct corresponding asymptotic confidence regions without full specification of the driving Lévy process. Second, by means of a polynomial type large deviation inequality we derive convergence of moments of our estimator under additional conditions.

    ジャーナルElectronic Journal of Statistics
    出版ステータス出版済み - 2010

    !!!All Science Journal Classification (ASJC) codes

    • 統計学および確率
    • 統計学、確率および不確実性


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