Question - What are two common techniques for detecting outliers?
Answer -
Analysts often use the following two techniques to detect outliers:
- Extreme value analysis. This is the most basic form of outlier detection and is limited to one-dimensional data. Extreme value analysis determines the statistical tails of the data distribution. The Altman Z-score is a good example of extreme value analysis.
- Probabilistic and statistical models. The models determine the unlikely instances from a probabilistic model of data. Data points with a low probability of membership are marked as outliers. However, these models assume that the data adheres to specific distributions. A common example of this type of outlier detection is the Bayesian probabilistic model.
These are only two of the core methods used to detect outliers. Other approaches include linear regression models, information theoretic models, high-dimensional outlier detection methods and other approaches.