Outfluence - The impact of missing values

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 measure can be used in a regression analysis context or any other parametric settings. We illustrate the major benefits of outfluence using two examples. The first example demonstrates the use of outfluence in a marijuana pilot study. The second example records blood cholesterol levels of heart attack victims at three time points after the heart attack. We evaluate the cholesterol level mean at time 3 and the difference between time 1 and time 3 with incomplete data.

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