IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v5y1956i3p203-206.html
   My bibliography  Save this article

Missing Values in Experiments Analysed on Automatic Computers

Author

Listed:
  • Michael Healy
  • Michael Westmacott

Abstract

Mr Healy and Mr Westmacott describe a general technique for dealing with observations missing from block experiments analysed on automatic computers. When more than one observation is missing the technique is simpler than other methods hitherto described and has been proved in practice to be satisfactorily fast. It is applicable to any analysis in which least‐squares estimates are derived.

Suggested Citation

  • Michael Healy & Michael Westmacott, 1956. "Missing Values in Experiments Analysed on Automatic Computers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 5(3), pages 203-206, November.
    • Handle: RePEc:bla:jorssc:v:5:y:1956:i:3:p:203-206
    DOI: 10.2307/2985421
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2985421
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2985421?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Avner Bar-Hen, 2002. "Influence of missing data on compact designs for spacing experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1229-1240.
    2. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    3. Yunquan Song & Yaqi Liu & Hang Su, 2022. "Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    4. Bindele, Huybrechts F., 2018. "Covariates missing at random under signed-rank inference," Econometrics and Statistics, Elsevier, vol. 8(C), pages 78-93.
    5. Carmen Anido & Carlos Rivero & Teofilo Valdes, 2011. "An algorithm for panel ANOVA with grouped data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(1), pages 85-107, July.
    6. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    7. Ash Abebe & Huybrechts F. Bindele & Masego Otlaadisa & Boikanyo Makubate, 2021. "Robust estimation of single index models with responses missing at random," Statistical Papers, Springer, vol. 62(5), pages 2195-2225, October.
    8. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    9. Encarnación Álvarez-Verdejo & Pablo J. Moya-Fernández & Juan F. Muñoz-Rosas, 2021. "Single Imputation Methods and Confidence Intervals for the Gini Index," Mathematics, MDPI, vol. 9(24), pages 1-20, December.
    10. Fulya Gokalp Yavuz & Olcay Arslan, 2018. "Linear mixed model with Laplace distribution (LLMM)," Statistical Papers, Springer, vol. 59(1), pages 271-289, March.
    11. Anido, Carmen & Rivero, Carlos & Valdés, Teófilo, 2008. "Analysis of variance with general errors and grouped and non-grouped data: Some iterative algorithms," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1544-1573, September.
    12. Rivero, Carlos & Valdes, Teofilo, 2008. "An algorithm for robust linear estimation with grouped data," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 255-271, December.
    13. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Other publications TiSEM 65401dae-613b-4e10-a8ae-c, Tilburg University, School of Economics and Management.
    14. Lu Li & Niwen Zhou & Lixing Zhu, 2022. "Outcome regression-based estimation of conditional average treatment effect," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 987-1041, October.

    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:jorssc:v:5:y:1956:i:3:p:203-206. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.