IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v214y2024ics0167715224001846.html
   My bibliography  Save this article

The integrated absolute error of the kernel error distribution estimator in the first-order autoregression model

Author

Listed:
  • Cheng, Fuxia

Abstract

This paper considers convergence rates of kernel estimators of the error cumulative distribution function in the first-order autoregressive model. LIL is extended to the integrated absolute error of residual-based kernel error cumulative distribution function estimator.

Suggested Citation

  • Cheng, Fuxia, 2024. "The integrated absolute error of the kernel error distribution estimator in the first-order autoregression model," Statistics & Probability Letters, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:stapro:v:214:y:2024:i:c:s0167715224001846
    DOI: 10.1016/j.spl.2024.110215
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715224001846
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2024.110215?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. Fuxia Cheng, 2024. "The Law of the Iterated Logarithm for L p -Norms of Kernel Estimators of Cumulative Distribution Functions," Mathematics, MDPI, vol. 12(7), pages 1-7, April.
    2. Cheng, Fuxia, 2018. "Glivenko–Cantelli Theorem for the kernel error distribution estimator in the first-order autoregressive model," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 95-102.
    3. Koul, Hira L. & Zhu, Zhiwei, 1995. "Bahadur-Kiefer representations for GM-estimators in autoregression models," Stochastic Processes and their Applications, Elsevier, vol. 57(1), pages 167-189, May.
    4. Gajek, L. & Kaluszka, M. & Lenic, A., 1996. "The law of the iterated logarithm for Lp-norms of empirical processes," Statistics & Probability Letters, Elsevier, vol. 28(2), pages 107-110, June.
    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. Liebscher, Eckhard, 2003. "Strong convergence of estimators in nonlinear autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 84(2), pages 247-261, February.
    2. Gao, Min & Yang, Wenzhi & Wu, Shipeng & Yu, Wei, 2022. "Asymptotic normality of residual density estimator in stationary and explosive autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Sinha, Sanjoy K. & Field, Christopher A. & Smith, Bruce, 2003. "Robust estimation of nonlinear regression with autoregressive errors," Statistics & Probability Letters, Elsevier, vol. 63(1), pages 49-59, May.
    4. Henze, Norbert & Nikitin, Yakov & Ebner, Bruno, 2009. "Integral distribution-free statistics of Lp-type and their asymptotic comparison," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3426-3438, July.
    5. Cheng, Fuxia, 2018. "Glivenko–Cantelli Theorem for the kernel error distribution estimator in the first-order autoregressive model," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 95-102.

    More about this item

    Keywords

    Kernel estimator; L1-norm; LIL; Residuals; Autoregressive models;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    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:214:y:2024:i:c:s0167715224001846. 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.