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Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
About this Title
Regina Y. Liu, Rutgers University, New Brunswick, NJ, Robert Serfling, University of Texas at Dallas, Richardson, TX and Diane L. Souvaine, Tufts University, Medford, MA, Editors
Publication: DIMACS Series in Discrete Mathematics and Theoretical Computer Science
Publication Year:
2006; Volume 72
ISBNs: 978-0-8218-3596-8 (print); 978-1-4704-4029-9 (online)
DOI: https://doi.org/10.1090/dimacs/072
MathSciNet review: MR2343124
MSC: Primary 62-06; Secondary 62Hxx
ReadershipTable of Contents
Front/Back Matter
Chapters
- Depth functions in nonparametric multivariate inference
- Rank tests for multivariate scale difference based on data depth
- On scale curves for nonparametric description of dispersion
- Data analysis and classification with the zonoid depth
- On some parametric, nonparametric and semiparametric discrimination rules
- Regression depth and support vector machine
- Spherical data depth and a multivariate median
- Depth-based classification for functional data
- Impartial trimmed means for functional data
- Geometric measures of data depth
- Computation of half-space depth using simulated annealing
- Primal-dual algorithms for data depth
- Simplicial depth: An improved definition, analysis, and efficiency for the finite sample case
- Fast algorithms for frames and point depth
- Statistical data depth and the graphics hardware