Exact and approximate computation of the scatter halfspace depth

Publication typeJournal Article
Publication date2024-05-09
scimago Q2
wos Q2
SJR0.750
CiteScore3.0
Impact factor1.4
ISSN09434062, 16139658
Abstract
The scatter halfspace depth (sHD) is an extension of the location halfspace (also called Tukey) depth that is applicable in the nonparametric analysis of scatter. Using sHD, it is possible to define minimax optimal robust scatter estimators for multivariate data. The problem of exact computation of sHD for data of dimension $$d \ge 2$$ has, however, not been addressed in the literature. We develop an exact algorithm for the computation of sHD in any dimension d and implement it efficiently for any dimension $$d \ge 1$$ . Since the exact computation of sHD is slow especially for higher dimensions, we also propose two fast approximate algorithms. All our programs are freely available in the R package scatterdepth.
Found 

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
0
Share
Cite this
GOST |
Cite this
GOST Copy
Liu X. et al. Exact and approximate computation of the scatter halfspace depth // Computational Statistics. 2024.
GOST all authors (up to 50) Copy
Liu X., Liu Y., Laketa P., Nagy S., Chen Y. Exact and approximate computation of the scatter halfspace depth // Computational Statistics. 2024.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1007/s00180-024-01500-6
UR - https://link.springer.com/10.1007/s00180-024-01500-6
TI - Exact and approximate computation of the scatter halfspace depth
T2 - Computational Statistics
AU - Liu, Xiaohui
AU - Liu, Yuzi
AU - Laketa, Petra
AU - Nagy, Stanislav
AU - Chen, Yuting
PY - 2024
DA - 2024/05/09
PB - Springer Nature
SN - 0943-4062
SN - 1613-9658
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Liu,
author = {Xiaohui Liu and Yuzi Liu and Petra Laketa and Stanislav Nagy and Yuting Chen},
title = {Exact and approximate computation of the scatter halfspace depth},
journal = {Computational Statistics},
year = {2024},
publisher = {Springer Nature},
month = {may},
url = {https://link.springer.com/10.1007/s00180-024-01500-6},
doi = {10.1007/s00180-024-01500-6}
}