IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v79y2017i5p1601-1618.html
   My bibliography  Save this article

Estimation of the common mean from heterogeneous normal observations with unknown variances

Author

Listed:
  • Andrew L. Rukhin

Abstract

No abstract is available for this item.

Suggested Citation

  • Andrew L. Rukhin, 2017. "Estimation of the common mean from heterogeneous normal observations with unknown variances," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1601-1618, November.
    • Handle: RePEc:bla:jorssb:v:79:y:2017:i:5:p:1601-1618
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssb.12227
    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.

    References listed on IDEAS

    as
    1. William E. Strawderman & Andrew L. Rukhin, 2010. "Simultaneous estimation and reduction of nonconformity in interlaboratory studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 219-234, March.
    2. Yuedong Wang & Yanyuan Ma & Raymond J. Carroll, 2009. "Variance estimation in the analysis of microarray data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 425-445, April.
    3. Rukhin, Andrew L., 2007. "Conservative confidence intervals based on weighted means statistics," Statistics & Probability Letters, Elsevier, vol. 77(8), pages 853-861, April.
    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. Bodnar, Olha & Bodnar, Taras, 2021. "Objective Bayesian meta-analysis based on generalized multivariate random effects model," Working Papers 2021:5, Örebro University, School of Business.
    2. Ahad Malekzadeh & Mahmood Kharrati-Kopaei, 2018. "Inferences on the common mean of several normal populations under heteroscedasticity," Computational Statistics, Springer, vol. 33(3), pages 1367-1384, September.

    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. Michela Battauz & Ruggero Bellio, 2011. "Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 40-56, January.
    2. Aurore Delaigle & Peter Hall, 2016. "Methodology for non-parametric deconvolution when the error distribution is unknown," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 231-252, January.
    3. J. R. Lockwood & Daniel F. McCaffrey, 2017. "Simulation-Extrapolation with Latent Heteroskedastic Error Variance," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 717-736, September.
    4. Dazard, Jean-Eudes & Sunil Rao, J., 2012. "Joint adaptive mean–variance regularization and variance stabilization of high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2317-2333.
    5. Bodnar, Olha & Bodnar, Taras, 2021. "Objective Bayesian meta-analysis based on generalized multivariate random effects model," Working Papers 2021:5, Örebro University, School of Business.
    6. Garcia, Tanya P. & Ma, Yanyuan, 2017. "Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models," Journal of Econometrics, Elsevier, vol. 200(2), pages 194-206.
    7. Yun Fang & Li-Xing Zhu, 2012. "Asymptotics of SIMEX-based variance estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(3), pages 329-345, April.
    8. Xiao Min & Chen Ting & Ming Ruixing & Huang Kunpeng, 2020. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing," Journal of Systems Science and Information, De Gruyter, vol. 8(6), pages 549-564, December.

    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:bla:jorssb:v:79:y:2017:i:5:p:1601-1618. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.