Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications最新文献

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Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications, Proceedings of a DIMACS Workshop, New Brunswick, New Jersey, USA, May 14-16, 2003 数据深度:鲁棒多元分析、计算几何及其应用,中国计算机科学与技术研讨会论文集,2003年5月14-16日,美国
Regina Y. Liu, R. Serfling, D. Souvaine
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引用次数: 67
Depth-based classification for functional data 基于深度的功能数据分类
S. López-Pintado, J. Romo
{"title":"Depth-based classification for functional data","authors":"S. López-Pintado, J. Romo","doi":"10.1090/dimacs/072/08","DOIUrl":"https://doi.org/10.1090/dimacs/072/08","url":null,"abstract":"Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods.","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130539150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 89
Primal-dual algorithms for data depth 数据深度的原对偶算法
David Bremner, K. Fukuda, V. Rosta
{"title":"Primal-dual algorithms for data depth","authors":"David Bremner, K. Fukuda, V. Rosta","doi":"10.1090/dimacs/072/12","DOIUrl":"https://doi.org/10.1090/dimacs/072/12","url":null,"abstract":"Note: PRO 060606 Reference ROSO-ARTICLE-2006-010 Record created on 2006-12-21, modified on 2016-08-08","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126586744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Regression depth and support vector machine 深度回归与支持向量机
A. Christmann
{"title":"Regression depth and support vector machine","authors":"A. Christmann","doi":"10.1090/dimacs/072/06","DOIUrl":"https://doi.org/10.1090/dimacs/072/06","url":null,"abstract":"The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression. Vapnik?s convex risk minimization principle [Vap98] has a dominating role in statistical machine learning theory. Important special cases are the support vector machine (SVM), [epsilon]-support vector regression and kernel logistic regression. In this paper connections between these methods from different disciplines are investigated for the case of pattern recognition. Some results concerning the robustness of the SVM and other kernel based methods are given.","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133483839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Rank tests for multivariate scale difference based on data depth 基于数据深度的多变量尺度差异的秩检验
Regina Y. Liu, Kesar Singh
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引用次数: 25
Spherical data depth and a multivariate median 球面数据深度和多元中位数
R. Elmore, T. Hettmansperger, F. Xuan
{"title":"Spherical data depth and a multivariate median","authors":"R. Elmore, T. Hettmansperger, F. Xuan","doi":"10.1090/dimacs/072/07","DOIUrl":"https://doi.org/10.1090/dimacs/072/07","url":null,"abstract":"","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131572023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
On some parametric, nonparametric and semiparametric discrimination rules 关于一些参数、非参数和半参数判别规则
Antti Hartikainen, H. Oja
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引用次数: 5
Impartial trimmed means for functional data 对功能数据进行公正的修剪
J. A. Cuesta-Albertos, R. Fraiman
{"title":"Impartial trimmed means for functional data","authors":"J. A. Cuesta-Albertos, R. Fraiman","doi":"10.1090/dimacs/072/09","DOIUrl":"https://doi.org/10.1090/dimacs/072/09","url":null,"abstract":"","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Fast algorithms for frames and point depth 帧和点深度的快速算法
J. Dulá
{"title":"Fast algorithms for frames and point depth","authors":"J. Dulá","doi":"10.1090/dimacs/072/14","DOIUrl":"https://doi.org/10.1090/dimacs/072/14","url":null,"abstract":"Foreword ix Preface xi Depth functions in nonparametric multivariate inference Robert Serfling 1 Rank tests for multivariate scale difference based on data depth Regina Y. Liu and Kesar Singh 17 On scale curves for nonparametric description of dispersion Jin Wang and Robert Serfling 37 Data analysis and classification with the zonoid depth Karl Mosler and Richard Hoberg 49 On some parametric, nonparametric and semiparametric discrimination rules Antti Hartikainen and Hannu Oja 61 Regression depth and support vector machine Andreas Christmann 71 Spherical data depth and a multivariate median Ryan T. Elmore, Thomas P. Hettmansperger, and Fengjuan Xuan 87 Depth-based classification for functional data Sara López-Pintado and Juan Romo 103 Impartial trimmed means for functional data Juan Antonio Cuesta-Albertos and Ricardo Fraiman 121 Geometric 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 Simplicial depth: An improved definition, analysis, and efficiency for the finite sample case Michael A. Burr, Eynat Rafalin, and Diane L. Souvaine 195","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128434949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data analysis and classification with the zonoid depth 数据分析与类带深度分类
K. Mosler, R. Hoberg
{"title":"Data analysis and classification with the zonoid depth","authors":"K. Mosler, R. Hoberg","doi":"10.1090/dimacs/072/04","DOIUrl":"https://doi.org/10.1090/dimacs/072/04","url":null,"abstract":"","PeriodicalId":161658,"journal":{"name":"Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123945677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 41
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