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Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
 
Edited by: Regina Y. Liu Rutgers University, New Brunswick, NJ
Robert Serfling University of Texas at Dallas, Richardson, TX
Diane L. Souvaine Tufts University, Medford, MA
A co-publication of the AMS and DIMACS
Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
eBook ISBN:  978-1-4704-4029-9
Product Code:  DIMACS/72.E
List Price: $102.00
MAA Member Price: $91.80
AMS Member Price: $81.60
Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
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Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
Edited by: Regina Y. Liu Rutgers University, New Brunswick, NJ
Robert Serfling University of Texas at Dallas, Richardson, TX
Diane L. Souvaine Tufts University, Medford, MA
A co-publication of the AMS and DIMACS
eBook ISBN:  978-1-4704-4029-9
Product Code:  DIMACS/72.E
List Price: $102.00
MAA Member Price: $91.80
AMS Member Price: $81.60
  • Book Details
     
     
    DIMACS - Series in Discrete Mathematics and Theoretical Computer Science
    Volume: 722006; 246 pp
    MSC: Primary 60; 62; 65; 68

    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 of the articles in the book lay down the foundations for further collaboration and interdisciplinary research.

    Co-published with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1–7 were co-published with the Association for Computer Machinery (ACM).

    Readership

    Graduate students and research mathematicians interested in multivariate analysis and computational geometry.

  • Table of Contents
     
     
    • 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
  • Additional Material
     
     
  • Reviews
     
     
    • This book clearly satisfies the goals of the editors: it contains state-of-the-art contributions on data depth that may be of interest to statisticians, mathematicians, computer scientists, and computational geometers. Connections between the different research fields are well exposed. This will certainly stimulate further interdisciplinary research.

      Biometrics
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Accessibility – to request an alternate format of an AMS title
Volume: 722006; 246 pp
MSC: Primary 60; 62; 65; 68

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 of the articles in the book lay down the foundations for further collaboration and interdisciplinary research.

Co-published with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1–7 were co-published with the Association for Computer Machinery (ACM).

Readership

Graduate students and research mathematicians interested in multivariate analysis and computational geometry.

  • 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
  • This book clearly satisfies the goals of the editors: it contains state-of-the-art contributions on data depth that may be of interest to statisticians, mathematicians, computer scientists, and computational geometers. Connections between the different research fields are well exposed. This will certainly stimulate further interdisciplinary research.

    Biometrics
Review Copy – for publishers of book reviews
Accessibility – to request an alternate format of an AMS title
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