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Prediciton regions for the visualization of incomplete datasets

Author

Listed:
  • Richard Dybowski

    (King’s College London (St Thomas Hospital Campus))

  • Peter R. Weller

    (City University)

Abstract

Summary A complication in the visualization of biomedical datasets is that they are often incomplete. A response to this is to multiply impute each missing datum prior to visualization in order to convey the uncertainty of the imputations. In our approach, the initially complete cases in a real-valued dataset are represented as points in a principal components plot and, for each initially incomplete case in the dataset, we use an associated prediction region or interval displayed on the same plot to indicate the probable location of the case. When a case has only one missing datum, a prediction interval is used in place of a region. The prediction region or interval associated with an incomplete case is determined from the dispersion of the multiple imputations of the case mapped onto the plot. We illustrate this approach with two incomplete datasets: the first is based on two multivariate normal distributions; the second on a published, simulated health survey.

Suggested Citation

  • Richard Dybowski & Peter R. Weller, 2001. "Prediciton regions for the visualization of incomplete datasets," Computational Statistics, Springer, vol. 16(1), pages 25-41, March.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_pl00022718
    DOI: 10.1007/PL00022718
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    References listed on IDEAS

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    1. Trivellore E. Raghunathan & David S. Siscovick, 1996. "A Multiple‐Imputation Analysis of a Case‐Control Study of the Risk of Primary Cardiac Arrest Among Pharmacologicallytreated Hypertensives," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(3), pages 335-352, September.
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