## 2. COPOD： COPOD: Copula-Based Outlier Detection

Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. IEEE International Conference on Data Mining (ICDM) , 2020.

COPOD使用了非参数（non-parametric）的方法，通过经验累积分布（Empirical CDF）来得到empirical copula，在这之后我们就可以简单的通过empirical copula来估算各个维度上的尾端概率。

## 3. 尾端概率与修正

1. 1-4行，计算empirical cdf，得到left tail和right tail，以及偏度

1. 6-15行，用copula得到左边和右边的尾端概率，并根据具体情况输出最适合的尾端概率

## 5. 使用与阅读

```# train the COPOD detector
from pyod.models.copod import COPOD
clf = COPOD()
clf.fit(X_train)
# get outlier scores
y_train_scores = clf.decision_scores_  # raw outlier scores
y_test_scores = clf.decision_function(X_test)  # outlier scores```

Arxiv精简版 ： COPOD: Copula-Based Outlier Detection

@inproceedings{li2020copod,

title={{COPOD:} Copula-Based Outlier Detection},

author={Li, Zheng and Zhao, Yue and Botta, Nicola and Ionescu, Cezar and Hu, Xiyang},

booktitle={IEEE International Conference on Data Mining (ICDM)},

year={2020},

organization={IEEE},

}