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Inferences on the Outfluence – How do Missing Values Impact Your Analysis?

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  • Ofer Harel
  • Jeffrey Stratton

Abstract

Incomplete data is a common complication of data analysis. Often researchers are interested in the impact of the missing data on their analysis (and therefore on their inferences). They might be interested in the impact of a single missing value, or a group of missing values on their inferences. The outfluence is a measure that can point to an observation or a group of observations that exert a disproportionate influence on the outcome of an analysis. In this article, we derive the asymptotic distribution of the outfluence and numerically show that the proposed asymptotic distribution agrees with a simulated one. We illustrate the major benefits of outfluence using a biomedical example.

Suggested Citation

  • Ofer Harel & Jeffrey Stratton, 2009. "Inferences on the Outfluence – How do Missing Values Impact Your Analysis?," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 38(16-17), pages 2884-2898, October.
    • Handle: RePEc:taf:lstaxx:v:38:y:2009:i:16-17:p:2884-2898
    DOI: 10.1080/03610920902947212
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