Data Depth: Robust Multivariate Analysis, Computational Geometry, and ApplicationsRegina 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). |
From inside the book
Results 1-5 of 68
Page vii
... measures of data depth GREG ALOUPIS 147 Computation of half - space depth using simulated annealing BIMAN CHAKRABORTY and PROBAL CHAUDHURI 159 Primal - dual algorithms for data depth DAVID BREMNER , KOMEI FUKUDA , and VERA ROSTA 171 ...
... measures of data depth GREG ALOUPIS 147 Computation of half - space depth using simulated annealing BIMAN CHAKRABORTY and PROBAL CHAUDHURI 159 Primal - dual algorithms for data depth DAVID BREMNER , KOMEI FUKUDA , and VERA ROSTA 171 ...
Page xi
... measures and sample statistics that can capture properly the higher - dimensional features of multivariate data are needed . Several geometric approaches have been proposed recently . Especially promising is the one founded on the ...
... measures and sample statistics that can capture properly the higher - dimensional features of multivariate data are needed . Several geometric approaches have been proposed recently . Especially promising is the one founded on the ...
Page 3
... measures . In particular , they provide a basis for generating outlyingness contours , for taking into account the geome- try of the data , and for defining a " center " and a corresponding center - outward ordering of points . Depth ...
... measures . In particular , they provide a basis for generating outlyingness contours , for taking into account the geome- try of the data , and for defining a " center " and a corresponding center - outward ordering of points . Depth ...
Page 4
... measure centrality or outlyingness . Its interpretation has no global perspective . It is sensitive to multimodality . The point of maximality is not interpretable as a " center " . Desirable properties of depth functions . Effective ...
... measure centrality or outlyingness . Its interpretation has no global perspective . It is sensitive to multimodality . The point of maximality is not interpretable as a " center " . Desirable properties of depth functions . Effective ...
Page 6
... data points . As a general class of examples , for given univariate location and scale statistics ( X ) and σ ( X ) , respectively , defined on X , and given " score " function that measures ( signed ) deviation from 0 in R ROBERT SERFLING.
... data points . As a general class of examples , for given univariate location and scale statistics ( X ) and σ ( X ) , respectively , defined on X , and given " score " function that measures ( signed ) deviation from 0 in R ROBERT SERFLING.
Contents
xi | |
1 | |
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 |
Depthbased classification for functional data | 103 |
Common terms and phrases
algorithm Annals of Statistics bivariate breakdown point buffer cell center-outward central region classification Computational Geometry Computer Science containing convergence convex hull Data Analysis data depth data points data set defined denote density depth contours depth function depth measures depth-based dimensional distribution dual error rate estimate example Figure finite functional data given halfspace depth hyperplane hyperplane arrangement integer programs iterations kernel Lemma linear location depth logistic regression lower bound Mathematics Mathematics Subject Classification matrix methods Multivariate Analysis multivariate data ncomplete O(n² optimal outlyingness parameter pixel point set problem PROOF properties quantile function random rank tests regression depth robust Rousseeuw sample scale curve Section Serfling simplicial depth simplicial median simulated spatial spherical depth spherical median stencil buffer subset support vector machine symmetric Theorem triangles trimmed mean Tukey univariate weak convergence zonoid depth
Popular passages
Page 17 - The discussion on aviation safety in this paper reflects the views of the authors, who are solely responsible for the accuracy of the analysis results presented herein, and does not necessarily reflect the official view or policy of the FAA.
Page 189 - K. Miller, S. Ramaswami, P. Rousseeuw, T. Sellares, D. Souvaine, I. Streinu and A. Struyf, Fast implementation of depth contours using topological sweep, Proceedings of the Twelfth ACM-SIAM Symposium on Discrete Algorithms, Washington, DC (2001), 690-699.
Page 157 - S. Jadhav and A. Mukhopadhyay. Computing a centerpoint of a finite planar set of points in linear time.
Page 34 - R. Liu, J. Parelius, and K. Singh, Multivariate analysis by data depth: descriptive statistics, graphics and inference (with discussions), Annals of Statistics 27 (1999), 783-858.
Page 188 - A. Marzetta, K. Fukuda and J. Nievergelt, The parallel search bench ZRAM and its applications, Annals of Operations Research (1999), 45-63.
Page 34 - Structural properties and convergence results for contours of sample statistical depth functions.
Page 168 - I. Ruts, and PJ Rousseeuw, Computing depth contours of bivariate point clouds, Computational Statistics and Data Analysis 23 (1996), 153-168.
Page 118 - Computing depth contours of bivariate point clouds. Computational Statistics and Data Analysis, 23, pp. 153-168. Struyf, A. and Rousseeuw, PJ (2000). High-dimensional computation of the deepest location. Computational Statistics and Data Analysis, to appear. Tukey, JW (1975), Mathematics and the picturing of data.
Page 85 - Steinwart. On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research, 2:67-93, 2002.