IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v32y1997i3p311-320.html
   My bibliography  Save this article

Some problems of nonparametric estimation by observations of ergodic diffusion process

Author

Listed:
  • Kutoyants, Yu. A.

Abstract

We consider the problems of the density and distribution function estimation by the observations of diffusion process with ergodic properties. In every problem we first propose a minimax bound on the risk of any estimator and then study the asymptotic behavior of several estimators. It is shown that the empiric distribution function is asymptotically normal and asymptotically efficient (in the minimax sense) estimator of the distribution function. In the density estimation problem, we describe the asymptotic behavior of a kernel-type estimator and one another (unbiased) estimator. Both of them are [radical sign]T-consistent, asymptotically normal and asymptotically efficient.

Suggested Citation

  • Kutoyants, Yu. A., 1997. "Some problems of nonparametric estimation by observations of ergodic diffusion process," Statistics & Probability Letters, Elsevier, vol. 32(3), pages 311-320, March.
    • Handle: RePEc:eee:stapro:v:32:y:1997:i:3:p:311-320
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(96)00088-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Castellana, J. V. & Leadbetter, M. R., 1986. "On smoothed probability density estimation for stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 21(2), pages 179-193, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guillou, Armelle & Merlevède, Florence, 2001. "Estimation of the Asymptotic Variance of Kernel Density Estimators for Continuous Time Processes," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 114-137, October.
    2. Comte, F. & Merlevède, F., 2005. "Super optimal rates for nonparametric density estimation via projection estimators," Stochastic Processes and their Applications, Elsevier, vol. 115(5), pages 797-826, May.
    3. Bosq, Denis & Merlevède, Florence & Peligrad, Magda, 1999. "Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time," Journal of Multivariate Analysis, Elsevier, vol. 68(1), pages 78-95, January.

    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. Dalalyan Arnak S. & Kutoyants Yury A., 2004. "On second order minimax estimation of invariant density for ergodic diffusion," Statistics & Risk Modeling, De Gruyter, vol. 22(1), pages 17-42, January.
    2. Labrador, Boris, 2008. "Strong pointwise consistency of the kT -occupation time density estimator," Statistics & Probability Letters, Elsevier, vol. 78(9), pages 1128-1137, July.
    3. Wu, Wei Biao & Huang, Yinxiao & Huang, Yibi, 2010. "Kernel estimation for time series: An asymptotic theory," Stochastic Processes and their Applications, Elsevier, vol. 120(12), pages 2412-2431, December.
    4. Oberhofer, Walter & Haupt, Harry, 2005. "The asymptotic distribution of the unconditional quantile estimator under dependence," Statistics & Probability Letters, Elsevier, vol. 73(3), pages 243-250, July.
    5. Robinson, Peter M. & Thawornkaiwong, Supachoke, 2012. "Statistical inference on regression with spatial dependence," Journal of Econometrics, Elsevier, vol. 167(2), pages 521-542.
    6. Guillou, Armelle & Merlevède, Florence, 2001. "Estimation of the Asymptotic Variance of Kernel Density Estimators for Continuous Time Processes," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 114-137, October.
    7. Natalia Markovich & Jorma Kilpi, 2009. "Bivariate statistical analysis of TCP-flow sizes and durations," Annals of Operations Research, Springer, vol. 170(1), pages 199-216, September.
    8. Negri, Ilia, 2001. "On efficient estimation of invariant density for ergodic diffusion processes," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 79-85, January.
    9. Mielniczuk, Jan, 1997. "On the asymptotic mean integrated squared error of a kernel density estimator for dependent data," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 53-58, May.
    10. Blanke, D. & Bosq, D., 1997. "Accurate rates of density estimators for continuous-time processes," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 185-191, April.
    11. Wang, Yizao & Woodroofe, Michael, 2014. "On the asymptotic normality of kernel density estimators for causal linear random fields," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 201-213.
    12. Sköld, Martin & Hössjer, Ola, 1999. "On the asymptotic variance of the continuous-time kernel density estimator," Statistics & Probability Letters, Elsevier, vol. 44(1), pages 97-106, August.
    13. Tomas Ruzgas & Mantas Lukauskas & Gedmantas Čepkauskas, 2021. "Nonparametric Multivariate Density Estimation: Case Study of Cauchy Mixture Model," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    14. Karine Bertin & Nicolas Klutchnikoff & Fabien Panloup & Maylis Varvenne, 2020. "Adaptive estimation of the stationary density of a stochastic differential equation driven by a fractional Brownian motion," Statistical Inference for Stochastic Processes, Springer, vol. 23(2), pages 271-300, July.
    15. Llop, P. & Forzani, L. & Fraiman, R., 2011. "On local times, density estimation and supervised classification from functional data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 73-86, January.
    16. Leblanc, Frédérique, 1996. "Wavelet linear density estimator for a discrete-time stochastic process: Lp-losses," Statistics & Probability Letters, Elsevier, vol. 27(1), pages 71-84, March.
    17. Didi Sultana & Louani Djamal, 2014. "Asymptotic results for the regression function estimate on continuous time stationary and ergodic data," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 129-150, June.
    18. Cheng, Yu-Hsiang & Huang, Tzee-Ming, 2012. "A conditional independence test for dependent data based on maximal conditional correlation," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 210-226.
    19. Zhan-Qian Lu, 1999. "Multivariate Local Polynomial Fitting for Martingale Nonlinear Regression Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(4), pages 691-706, December.
    20. Didi, Sultana & Louani, Djamal, 2013. "Consistency results for the kernel density estimate on continuous time stationary and dependent data," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1262-1270.

    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:32:y:1997:i:3:p:311-320. 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.