IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v64y2023i1d10.1007_s00362-022-01298-9.html
   My bibliography  Save this article

Admissible linear estimators in the general Gauss–Markov model under generalized extended balanced loss function

Author

Listed:
  • Buatikan Mirezi

    (Çukurova University)

  • Selahattin Kaçıranlar

    (Çukurova University)

Abstract

This paper proposes a new generalized extended balanced loss function (GEBLF). Admissibility of linear estimators is characterized in the General Gauss–Markov model with respect to GEBLF. The sufficient and necessary conditions for linear estimators to be admissible with a dispersion matrix possibly singular among the set of linear estimators are obtained. It is stated that the results obtained under special conditions lead to the results known in the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:64:y:2023:i:1:d:10.1007_s00362-022-01298-9
    DOI: 10.1007/s00362-022-01298-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-022-01298-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-022-01298-9?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. Stepniak, Czeslaw, 1989. "Admissible linear estimators in mixed linear models," Journal of Multivariate Analysis, Elsevier, vol. 31(1), pages 90-106, October.
    2. 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.
    3. Kazuhiro Ohtani & David Giles & Judith Giles, 1997. "The exact risk performance of a pre-test estimator in a heteroskedastic linear regression model under the balanced loss function," Econometric Reviews, Taylor & Francis Journals, vol. 16(1), pages 119-130.
    4. Markiewicz, Augustyn, 1996. "Characterization of general ridge estimators," Statistics & Probability Letters, Elsevier, vol. 27(2), pages 145-148, April.
    5. 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.
    6. Klonecki, W. & Zontek, S., 1988. "On the structure of admissible linear estimators," Journal of Multivariate Analysis, Elsevier, vol. 24(1), pages 11-30, January.
    7. Cao, Mingxiang, 2014. "Admissibility of linear estimators for the stochastic regression coefficient in a general Gauss–Markoff model under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 25-30.
    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. 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.
    2. Ewa Synówka-Bejenka & Stefan Zontek, 2008. "A characterization of admissible linear estimators of fixed and random effects in linear models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(2), pages 157-172, September.
    3. 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.
    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. Karamikabir, Hamid & Afshari, Mahmoud, 2020. "Generalized Bayesian shrinkage and wavelet estimation of location parameter for spherical distribution under balance-type loss: Minimaxity and admissibility," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    6. Hu, Guikai & Peng, Ping, 2012. "Matrix linear minimax estimators in a general multivariate linear model under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 286-295.
    7. 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.
    8. Hu, Guikai & Peng, Ping, 2011. "All admissible linear estimators of a regression coefficient under a balanced loss function," Journal of Multivariate Analysis, Elsevier, vol. 102(8), pages 1217-1224, September.
    9. Heiligers, Berthold & Markiewicz, Augustyn, 1996. "Linear sufficiency and admissibility in restricted linear models," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 105-111, October.
    10. Ibarrola, P. & Pérez-Palomares, A., 2003. "Linear sufficiency and linear admissibility in a continuous time Gauss-Markov model," Journal of Multivariate Analysis, Elsevier, vol. 87(2), pages 315-327, November.
    11. Koji Tsukuda & Hiroshi Kurata, 2020. "Covariance structure associated with an equality between two general ridge estimators," Statistical Papers, Springer, vol. 61(3), pages 1069-1084, June.
    12. 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.
    13. Markiewicz, Augustyn, 1998. "Comparison of linear restricted models with respect to the validity of admissible and linearly sufficient estimators," Statistics & Probability Letters, Elsevier, vol. 38(4), pages 347-354, July.
    14. Ibarrola, P. & Pérez-Palomares, A., 2004. "Linear completeness in a continuous time Gauss-Markov model," Statistics & Probability Letters, Elsevier, vol. 69(2), pages 143-149, August.

    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:spr:stpapr:v:64:y:2023:i:1:d:10.1007_s00362-022-01298-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.