https://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/20/lecture-20.pdf

Outliers and Influential Points

WHAT IS OUTLIER DETECTION: identification of points that are extreme in value

Outlier: large residual / departure in the vertical dimension

Leverage point: the extreme value of predictor variable / big difference in the horizontal dimension

Influence point: Removing observation substantially by changing estimate of coefficient

University of Michigan - Ann Arbor

Finding unusual patterns in the data

https://www.sciencedirect.com/topics/computer-science/outlier-detection

Outlier detection

- Isolation forest
- Robust random cut forest 
- Boxplots/Statistical Approach


Ways of detecting outliers

Outlier: data that differ dramatically from all others and distinguish themselves in characteristics.

Method 1 — Standard Deviation: data point that is more than 3 times the standard deviation, then those points are very likely to be outliers

Method 2 — Boxplots: Any data points that show above or below the whiskers, can be considered outliers.

Method 3— DBScan Clustering: Three core concepts (points) in DBScan:
Core Points
Border Points
Everything else is called Noise Points

(https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623)

3 Methods to detect Outlier. 

Outliers, Leverage Points and Influential Points


Which of the following methods is most suitable for anomaly detection in n (n > 1) dimensional space？

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