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Abstract:
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There are numerous measures that assess the effect of an observation, group of observations, a variable, or variables and observations on the regression estimation. Incomplete data is a common difficulty in data analysis. We introduce a new measure which assesses the effect of a missing observation, a group of missing observations, an incomplete variable or any combination of these, on the overall estimation. We call this measurement "outfluence." 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. The outfluence measure can be used in a regression analysis context or any other parametric settings. We illustrate the major benefits of outfluence using data example.
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