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On Lasso-type estimation for dynamical systems with small noise


  • Stefano Iacus

    (Department of Economics, Business and Statistics, University of Milan, IT)


We consider a dynamical system with small noise where the drift is parametrized by a finite dimensional parameter. For this model we consider minimum distance estimation from continuous time observations under some penalty imposed on the parameters in the spirit of the Lasso approach. This approach allows for simultaneous estimation and model selection for this model.

Suggested Citation

  • Stefano Iacus, 2010. "On Lasso-type estimation for dynamical systems with small noise," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1101, Universitá degli Studi di Milano.
  • Handle: RePEc:bep:unimip:unimi-1101 Note: oai:cdlib1:unimi-1101

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    References listed on IDEAS

    1. Kato, Kengo, 2009. "Asymptotics for argmin processes: Convexity arguments," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1816-1829, September.
    2. Kutoyants, Y. & Pilibossian, P., 1994. "On minimum L1-norm estimate of the parameter of the Ornstein--Uhlenbeck process," Statistics & Probability Letters, Elsevier, vol. 20(2), pages 117-123, May.
    3. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
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