Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105023
DC FieldValue
dc.titleAsymptotic distributions of the maximal depth estimators for regression and multivariate location
dc.contributor.authorBai, Z.-D.
dc.contributor.authorHe, X.
dc.date.accessioned2014-10-28T05:10:22Z
dc.date.available2014-10-28T05:10:22Z
dc.date.issued1999-10
dc.identifier.citationBai, Z.-D.,He, X. (1999-10). Asymptotic distributions of the maximal depth estimators for regression and multivariate location. Annals of Statistics 27 (5) : 1616-1637. ScholarBank@NUS Repository.
dc.identifier.issn00905364
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105023
dc.description.abstractWe derive the asymptotic distribution of the maximal depth regression estimator recently proposed in Rousseeuw and Hubert. The estimator is obtained by maximizing a projection-based depth and the limiting distribution is characterized through a max - min operation of a continuous process. The same techniques can be used to obtain the limiting distribution of some other depth estimators including Tukey's deepest point based on half-space depth. Results for the special case of two-dimensional problems have been available, but the earlier arguments have relied on some special geometric properties in the low-dimensional space. This paper completes the extension to higher dimensions for both regression and multivariate location models.
dc.sourceScopus
dc.subjectAsymptotic distribution
dc.subjectConsistency
dc.subjectEstimator
dc.subjectMedian
dc.subjectMultivariate location
dc.subjectRegression depth
dc.subjectRobustness
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleAnnals of Statistics
dc.description.volume27
dc.description.issue5
dc.description.page1616-1637
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Page view(s)

23
checked on Sep 7, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.