Data depth : robust multivariate analysis, computational geometry and applications
著者
書誌事項
Data depth : robust multivariate analysis, computational geometry and applications
（DIMACS series in discrete mathematics and theoretical computer science, v. 72）
American Mathematical Society, c2006
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注記
Includes bibliographical references
内容説明・目次
内容説明
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. 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 the Association for Computer Machinery (ACM).
目次
Depth functions in nonparametric multivariate inference by R. Serfling Rank tests for multivariate scale difference based on data depth by R. Y. Liu and K. Singh On scale curves for nonparametric description of dispersion by J. Wang and R. Serfling Data analysis and classification with the zonoid depth by K. Mosler and R. Hoberg On some parametric, nonparametric and semiparametric discrimination rules by A. Hartikainen and H. Oja Regression depth and support vector machine by A. Christmann Spherical data depth and a multivariate median by R. T. Elmore, T. P. Hettmansperger, and F. Xuan Depth-based classification for functional data by S. Lopez-Pintado and J. Romo Impartial trimmed means for functional data by J. A. Cuesta-Albertos and R. Fraiman Geometric measures of data depth by G. Aloupis Computation of half-space depth using simulated annealing by B. Chakraborty and P. Chaudhuri Primaldual algorithms for data depth by D. Bremner, K. Fukuda, and V. Rosta Simplicial depth: An improved definition, analysis, and efficiency for the finite sample case by M. A. Burr, E. Rafalin, and D. L. Souvaine Fast algorithms for frames and point depth by J. H. Dula Statistical data depth and the graphics hardware by S. Krishnan, N. H. Mustafa, and S. Venkatasubramanian.
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