IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v165y2019icp271-279.html
   My bibliography  Save this article

Computing the estimator of a parameter vector via a competing Bayes method

Author

Listed:
  • Wang, Lichun

Abstract

Bayesian analysis of normally distributed data with unknown mean and unknown variance is complicated. For the normal distribution N(μ,σ2), a linear Bayes procedure is suggested to simultaneously estimate the parameters μ and σ2. Compared with the usual Bayes estimator and the Lindley approximation, the proposed linear Bayes estimator is simple and easy to use, and some numerical examples are presented to verify its accuracies. Also, the superiorities of the linear Bayes estimator over classical estimators are established in terms of mean squared error matrix criterion.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:165:y:2019:i:c:p:271-279
    DOI: 10.1016/j.matcom.2019.03.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475419301077
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2019.03.011?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. 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.
    2. Chansoo Kim & Jinhyouk Jung & Younshik Chung, 2011. "Bayesian estimation for the exponentiated Weibull model under Type II progressive censoring," Statistical Papers, Springer, vol. 52(1), pages 53-70, February.
    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. Olayan Albalawi & Naresh Chandra Kabdwal & Qazi J. Azhad & Rashi Hora & Basim S. O. Alsaedi, 2022. "Estimation of the Generalized Logarithmic Transformation Exponential Distribution under Progressively Type-II Censored Data with Application to the COVID-19 Mortality Rates," Mathematics, MDPI, vol. 10(7), pages 1-19, March.
    2. A. Sikov & J. M. Stern, 2019. "Application of the full Bayesian significance test to model selection under informative sampling," Statistical Papers, Springer, vol. 60(1), pages 89-104, February.
    3. Fu, Jiayu & Xu, Ancha & Tang, Yincai, 2012. "Objective Bayesian analysis of Pareto distribution under progressive Type-II censoring," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1829-1836.
    4. Xuehua Hu & Wenhao Gui, 2018. "Bayesian and Non-Bayesian Inference for the Generalized Pareto Distribution Based on Progressive Type II Censored Sample," Mathematics, MDPI, vol. 6(12), pages 1-26, December.
    5. Saieed Ateya, 2014. "Maximum likelihood estimation under a finite mixture of generalized exponential distributions based on censored data," Statistical Papers, Springer, vol. 55(2), pages 311-325, May.
    6. Ruhul Ali Khan & Murari Mitra, 2021. "Estimation issues in the Exponential–Logarithmic model under hybrid censoring," Statistical Papers, Springer, vol. 62(1), pages 419-450, February.
    7. Kotb, M.S. & Raqab, M.Z., 2019. "Statistical inference for modified Weibull distribution based on progressively type-II censored data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 233-248.
    8. 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.
    9. Refah Alotaibi & Mazen Nassar & Hoda Rezk & Ahmed Elshahhat, 2022. "Inferences and Engineering Applications of Alpha Power Weibull Distribution Using Progressive Type-II Censoring," Mathematics, MDPI, vol. 10(16), pages 1-21, August.
    10. Rashad M. EL-Sagheer, 2018. "Estimation of parameters of Weibull–Gamma distribution based on progressively censored data," Statistical Papers, Springer, vol. 59(2), pages 725-757, June.
    11. Saralees Nadarajah & Gauss Cordeiro & Edwin Ortega, 2013. "The exponentiated Weibull distribution: a survey," Statistical Papers, Springer, vol. 54(3), pages 839-877, August.
    12. 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:matcom:v:165:y:2019:i:c:p:271-279. 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.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.