Outliers in Analysis

This is a post I wrote on company's internal wiki... just want a backup here.

Three points to bear in mind:

  1. Outliers are not bad; they just behave differently.
  2. Many statistics are sensitive to outliers. e.g. the mean and every model that relies on the mean.
  3. Abnormal detection is another topic; this post focus on exploring robust models.

Make the analysis robust to outliers:

Remove the outliers remove outliers according to a certain threshold or calculation (perhaps via unsupervised models); only focus on the left subsample. In most cases the signal will be more clear from concentrated subsample. Hard to generalize the effect to entire population; hard to define a threshold.
Capping the outliers cap the outliers to a certain value. All observations are kept so easy to map the effect to all samples afterward; outliers' impacts are punished. softer compared to removing the outliers; hard to find the threshold or capping rule.
Control for covariates include some control variables in the regression. some how analyze the "difference" but not just linear and constant difference. Introduce some relevant factors to gain a precise estimation and better learning. Need to find the right control variable. Irrelevant covariates will only introduce noise.
Regression to the median Median is more robust than the mean when outliers persist. Run a full quantile regression if possible; or just 50% quantile which is the median regression. Get a clear directional signal; robust to outliers so no need to choose any threshold. Hard to generalize the treatment effect to all population.
Quantile Regression Generalized model from above; help you understand subtle difference in each quantile. Gain more knowledge on the distribution rather than single point and great explanation power. Computational expensive; hard for further generalization;
Subsample Regression Instead of regress to each quantile, a subsample regression only run regression within each strata of the whole sample (say, sub-regression in each decile). Identical to introducing a categorical variable for each decile in regression; also help inspect each subsample. only directional; higher accumulated false-positive rate.
Take log() or other numerical transformations It's a numerical trick that shrink the range to a narrower one (high punishment on the high values). Easy to compute and map back to the real value; get directional results. May not be enough to obtain a clear signal.
Unsupervised study for higher dimensions This is more about outlier detection. When there are more than one dimension to define an outlier, some unsupervised models like K-means would help (identify the distance to the center). deals with higher dimensions exploration only.


Rank based methods Measure ranks of outcome variables instead of the absolute value. immunized to outliers. Hard to generalize the treatment effect to all population.


That's all I could think of for now...any addition is welcome 🙂

------- update on Apr 8, 2016 --------

Some new notes:

  1. "outliers" may not be the right name for heavy tails.
  2. Rank methods.

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