Data Depth: Robust Multivariate Analysis, Computational Geometry, and Applications

Front Cover
Regina Y. Liu, Robert Joseph Serfling, Diane L. Souvaine
American Mathematical Soc. - 246 pages
The book is a collection of some of the research presented at the workshop of the same name held in May 2003 at Rutgers University. The workshop brought together researchers from two different communities: statisticians and specialists in computational geometry. The main idea unifying these two research areas turned out to be the notion of data depth, which is an important notion both in statistics and in the study of efficiency of algorithms used in computational geometry. Many ofthe articles in the book lay down the foundations for further collaboration and interdisciplinary research. Information for our distributors: Co-published with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1-7 were co-published with theAssociation for Computer Machinery (ACM).
 

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Contents

Preface
xi
Depth functions in nonparametric multivariate inference
1
Rank tests for multivariate scale difference based on data depth
17
On scale curves for nonparametric description of dispersion
37
Data analysis and classification with the zonoid depth
49
On some parametric nonparametric and semiparametric discrimination rules
61
Regression depth and support vector machine
71
Spherical data depth and a multivariate median
87
Impartial trimmed means for functional data
121
Geometric measures of data depth
147
Computation of halfspace depth using simulated annealing
159
Primaldual algorithms for data depth
171
An improved definition analysis and efficiency for the finite sample case
195
Fast algorithms for frames and point depth
211
Statistical data depth and the graphics hardware
223
Copyright

Depthbased classification for functional data
103

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