Multivariate mode hunting: Data analytic tools with measures of significance
Saved in:
| Title: | Multivariate mode hunting: Data analytic tools with measures of significance |
|---|---|
| Authors: | Burman, Prabir1, Polonik, Wolfgang polonik@wald.ucdavis.edu |
| Source: | Journal of Multivariate Analysis. Jul2009, Vol. 100 Issue 6, p1198-1218. 21p. |
| Subjects: | Mathematical statistics, Multivariate analysis, Statistics, Hypothesis |
| Abstract: | Abstract: Multivariate mode hunting is of increasing practical importance. Only a few such methods exist, however, and there usually is a trade-off between practical feasibility and theoretical justification. In this paper we attempt to do both. We propose a method for locating isolated modes (or better, modal regions) in a multivariate data set without pre-specifying their total number. Information on significance of the findings is provided by means of formal testing for the presence of antimodes. Critical values of the tests are derived from large sample considerations. The method is designed to be computationally feasible in moderate dimensions, and it is complemented by diagnostic plots. Since the null hypothesis under consideration is highly composite the proposed tests involve calibration in order to ensure a correct (asymptotic) level. Our methods are illustrated by application to real data sets. [Copyright &y& Elsevier] |
| Copyright of Journal of Multivariate Analysis is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 37148985 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multivariate mode hunting: Data analytic tools with measures of significance – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burman%2C+Prabir%22">Burman, Prabir</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Polonik%2C+Wolfgang%22">Polonik, Wolfgang</searchLink><i> polonik@wald.ucdavis.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Multivariate+Analysis%22">Journal of Multivariate Analysis</searchLink>. Jul2009, Vol. 100 Issue 6, p1198-1218. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Hypothesis%22">Hypothesis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Multivariate mode hunting is of increasing practical importance. Only a few such methods exist, however, and there usually is a trade-off between practical feasibility and theoretical justification. In this paper we attempt to do both. We propose a method for locating isolated modes (or better, modal regions) in a multivariate data set without pre-specifying their total number. Information on significance of the findings is provided by means of formal testing for the presence of antimodes. Critical values of the tests are derived from large sample considerations. The method is designed to be computationally feasible in moderate dimensions, and it is complemented by diagnostic plots. Since the null hypothesis under consideration is highly composite the proposed tests involve calibration in order to ensure a correct (asymptotic) level. Our methods are illustrated by application to real data sets. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Multivariate Analysis is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=37148985 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jmva.2008.10.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1198 Subjects: – SubjectFull: Mathematical statistics Type: general – SubjectFull: Multivariate analysis Type: general – SubjectFull: Statistics Type: general – SubjectFull: Hypothesis Type: general Titles: – TitleFull: Multivariate mode hunting: Data analytic tools with measures of significance Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burman, Prabir – PersonEntity: Name: NameFull: Polonik, Wolfgang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 0047259X Numbering: – Type: volume Value: 100 – Type: issue Value: 6 Titles: – TitleFull: Journal of Multivariate Analysis Type: main |
| ResultId | 1 |
