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Robust parameter estimation for the Ornstein–Uhlenbeck process

  • Sonja Rieder


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    In this paper, we derive elementary M- and optimally robust asymptotic linear (AL)-estimates for the parameters of an Ornstein–Uhlenbeck process. Simulation and estimation of the process are already well-studied, see Iacus (Simulation and inference for stochastic differential equations. Springer, New York, 2008 ). However, in order to protect against outliers and deviations from the ideal law the formulation of suitable neighborhood models and a corresponding robustification of the estimators are necessary. As a measure of robustness, we consider the maximum asymptotic mean square error (maxasyMSE), which is determined by the influence curve (IC) of AL estimates. The IC represents the standardized influence of an individual observation on the estimator given the past. In a first step, we extend the method of M-estimation from Huber (Robust statistics. Wiley, New York, 1981 ). In a second step, we apply the general theory based on local asymptotic normality, AL estimates, and shrinking neighborhoods due to Kohl et al. (Stat Methods Appl 19:333–354, 2010 ), Rieder (Robust asymptotic statistics. Springer, New York, 1994 ), Rieder ( 2003 ), and Staab ( 1984 ). This leads to optimally robust ICs whose graph exhibits surprising behavior. In the end, we discuss the estimator construction, i.e. the problem of constructing an estimator from the family of optimal ICs. Therefore we carry out in our context the One-Step construction dating back to LeCam (Asymptotic methods in statistical decision theory. Springer, New York, 1969 ) and compare it by means of simulations with MLE and M-estimator. Copyright Springer-Verlag 2012

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    Article provided by Springer in its journal Statistical Methods & Applications.

    Volume (Year): 21 (2012)
    Issue (Month): 4 (November)
    Pages: 411-436

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    Handle: RePEc:spr:stmapp:v:21:y:2012:i:4:p:411-436
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    1. Loriano Mancini & Elvezio Ronchetti & Fabio Trojani, 2004. "Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.04, Institut d'Economie et Econométrie, Université de Genève.
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    6. Matthias Kohl & Peter Ruckdeschel & Helmut Rieder, 2010. "Infinitesimally Robust estimation in general smoothly parametrized models," Statistical Methods and Applications, Springer, vol. 19(3), pages 333-354, August.
    7. Marc Hallin & Christophe Koell & Bas Werker, 2000. "Optimal inference for discretely observed semiparametric Ornstein-Uhlenbeck processes," ULB Institutional Repository 2013/2097, ULB -- Universite Libre de Bruxelles.
    8. Tadeusz Bednarski, 2010. "Fréchet differentiability in statistical inference for time series," Statistical Methods and Applications, Springer, vol. 19(4), pages 517-528, November.
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    11. Nakahiro Yoshida, 1990. "Asymptotic behavior of M-estimator and related random field for diffusion process," Annals of the Institute of Statistical Mathematics, Springer, vol. 42(2), pages 221-251, June.
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