IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v71y2014icp633-642.html
   My bibliography  Save this article

Linear Bayes estimator for the two-parameter exponential family under type II censoring

Author

Listed:
  • Wang, Lichun
  • Singh, Radhey S.

Abstract

For the two-parameter exponential family, a linear Bayes method is proposed to simultaneously estimate the parameter vector consisting of location and scale parameters. The superiority of the proposed linear Bayes estimator (LBE) over the classical UMVUE is established in terms of the mean square error matrix (MSEM) criterion. The proposed LBE is simple and easy to use compared with the usual Bayes estimator, which is obtained by the MCMC method. Numerical results are presented to verify that the LBE works well. In the empirical Bayes framework, the paper invokes a linear empirical Bayes estimator (LEBE) by using a linear combination of historical samples. It is shown under some mild regularity conditions that the LEBE is superior to the classical UMVUE and the maximum likelihood estimator in terms of MSEM. It is further shown with numerical results that the performance of LEBE gets better with the increase in the number of historical samples.

Suggested Citation

  • Wang, Lichun & Singh, Radhey S., 2014. "Linear Bayes estimator for the two-parameter exponential family under type II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 633-642.
  • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:633-642
    DOI: 10.1016/j.csda.2013.07.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794731300265X
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2013.07.020?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. Minoda, Yuta & Yanagimoto, Takemi, 2009. "Estimation of a common slope in a gamma regression model with multiple strata: An empirical Bayes method," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4178-4185, October.
    2. Maritz, J. S., 1989. "Linear empirical Bayes estimation of quantiles," Statistics & Probability Letters, Elsevier, vol. 8(1), pages 59-65, May.
    3. Huang, Wen-Tao & Huang, Hui-Hsin, 2006. "Empirical Bayes estimation of the guarantee lifetime in a two-parameter exponential distribution," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1821-1829, October.
    4. Samaniego, Francisco J. & Vestrup, Eric, 1999. "On improving standard estimators via linear empirical Bayes methods," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 309-318, September.
    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. Wang, Lichun, 2019. "Computing the estimator of a parameter vector via a competing Bayes method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 165(C), pages 271-279.
    2. Takatoshi Sugiyama & Toru Ogura, 2022. "Parameters Estimation for Wear-out Failure Period of Three-Parameter Weibull Distribution," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(1), pages 1-40, March.
    3. Pitselis, Georgios, 2017. "Risk measures in a quantile regression credibility framework with Fama/French data applications," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 122-134.
    4. Pitselis, Georgios, 2013. "Quantile credibility models," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 477-489.
    5. Pitselis, Georgios, 2016. "Credible risk measures with applications in actuarial sciences and finance," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 373-386.
    6. Lee-Shen Chen, 2009. "On empirical Bayes two-tail tests for double exponential distributions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 1037-1049.
    7. Allison, David B. & Gadbury, Gary L. & Heo, Moonseong & Fernandez, Jose R. & Lee, Cheol-Koo & Prolla, Tomas A. & Weindruch, Richard, 2002. "A mixture model approach for the analysis of microarray gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 1-20, March.

    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:csdana:v:71:y:2014:i:c:p:633-642. 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/csda .

    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.