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Missing Values in Experiments Analysed on Automatic Computers

Author

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  • 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
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    Citations

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    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. Fulya Gokalp Yavuz & Olcay Arslan, 2018. "Linear mixed model with Laplace distribution (LLMM)," Statistical Papers, Springer, vol. 59(1), pages 271-289, March.
    3. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    4. 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.
    5. 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.
    6. Bindele, Huybrechts F., 2018. "Covariates missing at random under signed-rank inference," Econometrics and Statistics, Elsevier, vol. 8(C), pages 78-93.
    7. 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.
    8. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    9. 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.
    10. 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.
    11. 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.
    12. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    13. 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.
    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.

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