https://github.com/mnarayan/ddplot

Depth vs. Depth Plots
https://github.com/mnarayan/ddplot

Last synced: 7 days ago
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Depth vs. Depth Plots

• Host: GitHub
• URL: https://github.com/mnarayan/ddplot
• Owner: mnarayan
• License: bsd-3-clause
• Created: 2018-04-04T02:29:25.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-04-04T02:31:17.000Z (over 6 years ago)
• Last Synced: 2024-12-13T12:09:43.856Z (9 days ago)
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Depth vs. Depth Plots 

## Choice of Data Depth 

- *Mahalanobis* Depth

- *Half Space* Depth

- *Projection* Depth

- *Simplicial* Depth

Note: Mahalanobis depth does not do well for elliptical and non-elliptical distributions, projection depth is ideal for elliptical distributions while half-space or simplicial depths do well for non-elliptical distributions.

## References

- Liu, R. (1990), “On a Notion of Data Depth Based on Random Simplices,” The Annals of Statistics, 18, 405–414. 

- Liu, R. (1992), “Data Depth and Multivariate Rank Tests,” in L1-Statistical Analysis and Related Methods, ed. Y. Dodge, Amsterdam: North-Holland, pp. 279–294. 

- Liu, R., Parelius, J., and Singh, K. (1999), “Multivariate Analysis by Data Depth: Descriptive Statistics, Graphics and Inference" (with discussion), Ann. Statist. Volume 27, Number 3 (1999), 783-858. doi:[10.1214/aos/1018031260](https://projecteuclid.org/euclid.aos/1018031260)

- Jun Li , Juan A. Cuesta-Albertos & Regina Y. Liu (2012) DD-Classifier: Nonparametric Classification Procedure Based on DD-Plot, JASA, 107:498, 737-753, doi:[10.1080/01621459.2012.688462](https://doi.org/10.1080/01621459.2012.688462)

> In this article, we use Mahalanobis depth to cover the well-studied Gaussian

case, and half-space depth, simplicial depth, and projection

depth to explore the robustness aspect of our approach. The last

three depths are geometric and thus completely nonparametric.