IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v88y1998i1p193-201.html
   My bibliography  Save this article

Inadmissibility of the Stein-rule estimator under the balanced loss function

Author

Listed:
  • Ohtani, Kazuhiro

Abstract

No abstract is available for this item.

Suggested Citation

  • Ohtani, Kazuhiro, 1998. "Inadmissibility of the Stein-rule estimator under the balanced loss function," Journal of Econometrics, Elsevier, vol. 88(1), pages 193-201, November.
    • Handle: RePEc:eee:econom:v:88:y:1998:i:1:p:193-201
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(98)00030-X
    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. Wan, Alan T. K., 1994. "Risk comparison of the inequality constrained least squares and other related estimators under balanced loss," Economics Letters, Elsevier, vol. 46(3), pages 203-210, November.
    2. Gelfand, Alan E. & Dey, Dipak K., 1988. "Improved estimation of the disturbance variance in a linear regression model," Journal of Econometrics, Elsevier, vol. 39(3), pages 387-395, November.
    3. Ohtani, Kazuhiro, 1996. "Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model," Statistics & Probability Letters, Elsevier, vol. 29(3), pages 191-199, September.
    4. Ohtani, Kazuhiro & Kozumi, Hideo, 1996. "The exact general formulae for the moments and the MSE dominance of the Stein-rule and positive-part Stein-rule estimators," Journal of Econometrics, Elsevier, vol. 74(2), pages 273-287, October.
    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. Cao, Ming-Xiang & He, Dao-Jiang, 2017. "Admissibility of linear estimators of the common mean parameter in general linear models under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 246-254.
    2. Chaturvedi, Anoop & Shalabh, 2004. "Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 229-256, August.
    3. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.

    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. Akio Namba, 2003. "On the use of the Stein variance estimator in the double k-class estimator when each individual regression coefficient is estimated," Statistical Papers, Springer, vol. 44(1), pages 117-124, January.
    2. Kazuhiro Ohtani & Alan Wan, 2002. "ON THE USE OF THE STEIN VARIANCE ESTIMATOR IN THE DOUBLE k-CLASS ESTIMATOR IN REGRESSION," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 121-134.
    3. Qin, Huaizhen & Ouyang, Weiwei, 2016. "Asymmetric risk of the Stein variance estimator under a misspecified linear regression model," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 94-100.
    4. Zhu, Rong & Zhou, Sherry Z.F., 2011. "Estimating the error variance after a pre-test for an interval restriction on the coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2312-2323, July.
    5. Shalabh, 2001. "Least squares estimators in measurement error models under the balanced loss function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(2), pages 301-308, December.
    6. Ohtani, Kazuhiro, 1996. "Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model," Statistics & Probability Letters, Elsevier, vol. 29(3), pages 191-199, September.
    7. Buatikan Mirezi & Selahattin Kaçıranlar, 2023. "Admissible linear estimators in the general Gauss–Markov model under generalized extended balanced loss function," Statistical Papers, Springer, vol. 64(1), pages 73-92, February.
    8. Ohtani, Kazuhiro, 2002. "Exact distribution of a pre-test estimator for regression error variance when there are omitted variables," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 129-140, November.
    9. Wan, Alan T. K. & Zou, Guohua, 2003. "Optimal critical values of pre-tests when estimating the regression error variance: analytical findings under a general loss structure," Journal of Econometrics, Elsevier, vol. 114(1), pages 165-196, May.
    10. Kazuhiro Ohtani & Alan Wan, 2009. "Comparison of the Stein and the usual estimators for the regression error variance under the Pitman nearness criterion when variables are omitted," Statistical Papers, Springer, vol. 50(1), pages 151-160, January.
    11. Akio Namba, 2015. "MSE dominance of the positive-part shrinkage estimator when each individual regression coefficient is estimated," Statistical Papers, Springer, vol. 56(2), pages 379-390, May.
    12. Chaturvedi, Anoop & Shalabh, 2004. "Risk and Pitman closeness properties of feasible generalized double k-class estimators in linear regression models with non-spherical disturbances under balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 90(2), pages 229-256, August.
    13. D. Gokhale & Koichi Inada & Hea-Jung Kim, 1993. "A minimum discrimination information estimator of preliminary conjectured normal variance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 129-136, March.
    14. Youhei Oono & Nobuo Shinozaki, 2006. "Estimation of error variance in ANOVA model and order restricted scale parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(4), pages 739-756, December.
    15. Hu, Guikai & Yu, Shenghua & Luo, Han, 2015. "Comparisons of variance estimators in a misspecified linear model with elliptically contoured errors," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 266-276.
    16. Akio Namba & Kazuhiro Ohtani, 2007. "Risk comparison of the Stein-rule estimator in a linear regression model with omitted relevant regressors and multivariatet errors under the Pitman nearness criterion," Statistical Papers, Springer, vol. 48(1), pages 151-162, January.

    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:eee:econom:v:88:y:1998:i:1:p:193-201. 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/locate/jeconom .

    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.