IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v41y1992i2p281-296.html
   My bibliography  Save this article

Kernel density estimation for linear processes

Author

Listed:
  • Tran, Lanh Tat

Abstract

Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} is an innovation process consisting of independent and identically distributed random variables with mean zero and finite variance. Assume that X1 has a probability density [latin small letter f with hook]. Uniform strong consistency of kernel density estimators of [latin small letter f with hook] is established, and their rates of convergence are obtained. The estimators can achieve the rate of convergence (n-1 log n)1/3 in L[infinity] norm restricted to compacts under weak conditions.

Suggested Citation

  • Tran, Lanh Tat, 1992. "Kernel density estimation for linear processes," Stochastic Processes and their Applications, Elsevier, vol. 41(2), pages 281-296, June.
    • Handle: RePEc:eee:spapps:v:41:y:1992:i:2:p:281-296
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0304-4149(92)90128-D
    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.

    Citations

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


    Cited by:

    1. Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
    2. Timothy Fortune & Hailin Sang, 2020. "Shannon Entropy Estimation for Linear Processes," JRFM, MDPI, vol. 13(9), pages 1-13, September.
    3. Anton Schick & Wolfgang Wefelmeyer, 2008. "Root-n consistency in weighted L 1 -spaces for density estimators of invertible linear processes," Statistical Inference for Stochastic Processes, Springer, vol. 11(3), pages 281-310, October.
    4. Hallin, Marc & Lu, Zudi & Tran, Lanh T., 2004. "Kernel density estimation for spatial processes: the L1 theory," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 61-75, January.
    5. Schick, Anton & Wefelmeyer, Wolfgang, 2007. "Prediction in invertible linear processes," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1322-1331, July.
    6. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2015. "Estimators in step regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 124-129.
    7. Ho, Hwai-Chung, 1995. "On the strong uniform consistency of density estimation for strongly dependent sequences," Statistics & Probability Letters, Elsevier, vol. 22(2), pages 149-156, February.
    8. Livasoa Andriamampionona & Victor Harison & Michel Harel, 2024. "Non-Parametric Estimation of the Renewal Function for Multidimensional Random Fields," Mathematics, MDPI, vol. 12(12), pages 1-22, June.
    9. Hwang, Eunju & Shin, Dong Wan, 2012. "Stationary bootstrap for kernel density estimators under ψ-weak dependence," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1581-1593.
    10. Masry, Elias, 1997. "Multivariate probability density estimation by wavelet methods: Strong consistency and rates for stationary time series," Stochastic Processes and their Applications, Elsevier, vol. 67(2), pages 177-193, May.
    11. Schick, Anton & Wefelmeyer, Wolfgang, 2006. "Pointwise convergence rates and central limit theorems for kernel density estimators in linear processes," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1756-1760, October.

    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:spapps:v:41:y:1992:i:2:p:281-296. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/505572/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.