IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v92y2005i3p737-746.html
   My bibliography  Save this article

The Stein–James estimator for short- and long-memory Gaussian processes

Author

Listed:
  • Masanobu Taniguchi
  • Junichi Hirukawa

Abstract

We investigate the mean squared error of the Stein--James estimator for the mean when the observations are generated from a Gaussian vector stationary process with dimension greater than two. First, assuming that the process is short-memory, we evaluate the mean squared error, and compare it with that for the sample mean. Then a sufficient condition for the Stein--James estimator to improve upon the sample mean is given in terms of the spectral density matrix around the origin. We repeat the analysis for Gaussian vector long-memory processes. Numerical examples clearly illuminate the Stein--James phenomenon for dependent samples. The results have the potential to improve the usual trend estimator in time series regression models. Copyright 2005, Oxford University Press.

Suggested Citation

  • Masanobu Taniguchi & Junichi Hirukawa, 2005. "The Stein–James estimator for short- and long-memory Gaussian processes," Biometrika, Biometrika Trust, vol. 92(3), pages 737-746, September.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:737-746
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/92.3.737
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Shiraishi, Hiroshi & Taniguchi, Masanobu & Yamashita, Takashi, 2018. "Higher-order asymptotic theory of shrinkage estimation for general statistical models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 198-211.
    2. Yan Liu & Yoshiyuki Tanida & Masanobu Taniguchi, 2021. "Shrinkage estimation for multivariate time series," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 733-751, October.
    3. Li, Ming & Li, Jia-Yue, 2017. "Generalized Cauchy model of sea level fluctuations with long-range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 309-335.
    4. Senda, Motohiro & Taniguchi, Masanobu, 2006. "James-Stein estimators for time series regression models," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 1984-1996, October.

    More about this item

    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:oup:biomet:v:92:y:2005:i:3:p:737-746. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.