IDEAS home Printed from https://ideas.repec.org/a/spr/sistpr/v21y2018i3d10.1007_s11203-017-9157-5.html
   My bibliography  Save this article

Local asymptotic normality for shape and periodicity in the drift of a time inhomogeneous diffusion

Author

Listed:
  • Simon Holbach

    (Johannes Gutenberg-Universität Mainz)

Abstract

We consider a one-dimensional diffusion whose drift contains a deterministic periodic signal with unknown periodicity T and carrying some unknown d-dimensional shape parameter $$\vartheta $$ ϑ . We prove local asymptotic normality (LAN) jointly in $$\vartheta $$ ϑ and T for the statistical experiment arising from continuous observation of this diffusion. The local scale turns out to be $$n^{-1/2}$$ n - 1 / 2 for the shape parameter and $$n^{-3/2}$$ n - 3 / 2 for the periodicity which generalizes known results about LAN when either $$\vartheta $$ ϑ or T is assumed to be known.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:sistpr:v:21:y:2018:i:3:d:10.1007_s11203-017-9157-5
    DOI: 10.1007/s11203-017-9157-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11203-017-9157-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11203-017-9157-5?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. Herold Dehling & Brice Franke & Thomas Kott, 2010. "Drift estimation for a periodic mean reversion process," Statistical Inference for Stochastic Processes, Springer, vol. 13(3), pages 175-192, October.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Rachid Belfadli & Khalifa Es-Sebaiy & Fatima-Ezzahra Farah, 2022. "Statistical analysis of the non-ergodic fractional Ornstein–Uhlenbeck process with periodic mean," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(7), pages 885-911, October.
    7. Pramesti Getut, 2023. "Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process," Monte Carlo Methods and Applications, De Gruyter, vol. 29(1), pages 1-32, March.
    8. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.
    9. 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.
    10. Höpfner Reinhard & Kutoyants Yury A., 2009. "On LAN for parametrized continuous periodic signals in a time inhomogeneous diffusion," Statistics & Risk Modeling, De Gruyter, vol. 27(4), pages 309-326, December.
    11. Reinhard Höpfner, 2021. "Polynomials under Ornstein–Uhlenbeck noise and an application to inference in stochastic Hodgkin–Huxley systems," Statistical Inference for Stochastic Processes, Springer, vol. 24(1), pages 35-59, April.
    12. Yunhong Lyu & Sévérien Nkurunziza, 2023. "Inference in generalized exponential O–U processes," Statistical Inference for Stochastic Processes, Springer, vol. 26(3), pages 581-618, October.
    13. Sévérien Nkurunziza & Lei Shen, 2020. "Inference in a multivariate generalized mean-reverting process with a change-point," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 199-226, April.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Herold Dehling & Brice Franke & Jeannette H. C. Woerner, 2017. "Estimating drift parameters in a fractional Ornstein Uhlenbeck process with periodic mean," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 1-14, April.
    19. Sévérien Nkurunziza, 2023. "Estimation and Testing in Multivariate Generalized Ornstein-Uhlenbeck Processes with Change-Points," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 351-400, February.
    20. 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.

    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:spr:sistpr:v:21:y:2018:i:3:d:10.1007_s11203-017-9157-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.