Robust statistics in data analysis — A review: Basic concepts

By Michael Daszykowski, Krzysztof Kaczmarek, Yvan Vander Heyden, Beata Walczak. 2007

362 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ea7edb77adc305fab0b9969ca6b6875f19a7bdd4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Biao Huang
2. Steven X. Ding
3. Zhiqiang Ge
4. Zhihuan Song
5. Hui Huang

In Statistics and Probability

1. Biao Huang
2. Steven X. Ding
3. Zhiqiang Ge
4. Zhihuan Song
5. Hao Zhang

Citations in the corpus

• L1-medial skeleton of point cloud (2013, background): “It represents a unique global center of a given set of points [Daszykowski et al. 2007], with the prominent property that it is robust to outliers and noise.”
• Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, methodology): “Besides, several robust PCA methods were developed and applied for outlier detection in the process industry [50]–[52].”
• Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification (2014, background): “The geometric median is known as a robust center of an arbitrary point set and has the prominent property of sampling insensitivity (Daszykowski et al., 2007).”