IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v83y2013i2p608-615.html
   My bibliography  Save this article

Nonparametric estimation problem for a time-periodic signal in a periodic noise

Author

Listed:
  • Dehay, D.
  • El Waled, K.

Abstract

In this paper we construct a kernel estimator of a periodic signal when the observation follows the model dζt=f(t)dt+σ(t)dWt, where f,σ:R→R are continuous periodic and {Wt,t≥0} is a Brownian motion. We state its consistency as well as the asymptotic normality.

Suggested Citation

  • Dehay, D. & El Waled, K., 2013. "Nonparametric estimation problem for a time-periodic signal in a periodic noise," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 608-615.
  • Handle: RePEc:eee:stapro:v:83:y:2013:i:2:p:608-615
    DOI: 10.1016/j.spl.2012.11.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715212004245
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spl.2012.11.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Reinhard Höpfner & Yury Kutoyants, 2010. "Estimating discontinuous periodic signals in a time inhomogeneous diffusion," Statistical Inference for Stochastic Processes, Springer, vol. 13(3), pages 193-230, October.
    2. Jankunas, Andrius & Khasminskii, Rafail Z., 1997. "Estimation of parameters of linear homogeneous stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 72(2), pages 205-219, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mishura, Yuliya, 2014. "Standard maximum likelihood drift parameter estimator in the homogeneous diffusion model is always strongly consistent," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 24-29.
    2. Sergueï Dachian & Ilia Negri, 2011. "On compound Poisson processes arising in change-point type statistical models as limiting likelihood ratios," Statistical Inference for Stochastic Processes, Springer, vol. 14(3), pages 255-271, October.
    3. Dominique Dehay, 2015. "Parameter maximum likelihood estimation problem for time periodic modulated drift Ornstein Uhlenbeck processes," Statistical Inference for Stochastic Processes, Springer, vol. 18(1), pages 69-98, April.
    4. E. A. Pchelintsev & S. M. Pergamenshchikov, 2018. "Oracle inequalities for the stochastic differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 469-483, July.
    5. Loukianova, D. & Loukianov, O., 2005. "Uniform law of large numbers and consistency of estimators for Harris diffusions," Statistics & Probability Letters, Elsevier, vol. 74(4), pages 347-355, October.
    6. Herold Dehling & Brice Franke & Thomas Kott & Reg Kulperger, 2014. "Change point testing for the drift parameters of a periodic mean reversion process," Statistical Inference for Stochastic Processes, Springer, vol. 17(1), pages 1-18, April.
    7. Oçafrain, William, 2020. "Q-processes and asymptotic properties of Markov processes conditioned not to hit moving boundaries," Stochastic Processes and their Applications, Elsevier, vol. 130(6), pages 3445-3476.
    8. Victor, Konev & Serguei, Pergamenchtchikov, 2015. "Robust model selection for a semimartingale continuous time regression from discrete data," Stochastic Processes and their Applications, Elsevier, vol. 125(1), pages 294-326.
    9. Vlad Stefan Barbu & Slim Beltaief & Sergey Pergamenshchikov, 2019. "Robust adaptive efficient estimation for semi-Markov nonparametric regression models," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 187-231, July.
    10. Simon Holbach, 2018. "Local asymptotic normality for shape and periodicity in the drift of a time inhomogeneous diffusion," Statistical Inference for Stochastic Processes, Springer, vol. 21(3), pages 527-538, October.
    11. Andrius Jankunas, 1999. "Local Asymptotic Normality for Linear Homogeneous Difference Equations with Non-Gaussian Noise," Journal of Theoretical Probability, Springer, vol. 12(3), pages 675-697, July.
    12. Yang, Xiangfeng & Liu, Yuhan & Park, Gyei-Kark, 2020. "Parameter estimation of uncertain differential equation with application to financial market," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    13. Sévérien Nkurunziza & Pei Patrick Zhang, 2018. "Estimation and testing in generalized mean-reverting processes with change-point," Statistical Inference for Stochastic Processes, Springer, vol. 21(1), pages 191-215, April.
    14. Evgeny Pchelintsev & Serguei Pergamenshchikov & Maria Leshchinskaya, 2022. "Improved estimation method for high dimension semimartingale regression models based on discrete data," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 537-576, October.
    15. R. Z. Khasminskii & N. V. Krylov, 2022. "On the asymptotic behavior of solutions of the Cauchy problem for parabolic equations with time periodic coefficients," Statistical Inference for Stochastic Processes, Springer, vol. 25(1), pages 3-16, April.
    16. N. Lin & S. Lototsky, 2014. "Second-order continuous-time non-stationary Gaussian autoregression," Statistical Inference for Stochastic Processes, Springer, vol. 17(1), pages 19-49, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:83:y:2013:i:2:p:608-615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.