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General notions of depth for functional ...

Mosler, Karl...

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General notions of depth for functional data by Mosler, Karl ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	27-07-2023
Summary	A data depth measures the centrality of a point with respect to an empirical distribution. Postulates are formulated, which a depth for functional data should satisfy, and a general approach is proposed to construct multivariate data depths in Banach spaces. The new approach, mentioned as Phi-depth, is based on depth infima over a proper set Phi of R^d-valued linear functions. Several desirable properties are established for the Phi-depth and a generalized version of it. The general notions include many new depths as special cases. In particular a location-slope depth and a principal component depth are introduced.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1208.1981

Citation

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AMA 11th EditionReferences

Mosler, Karl. General notions of depth for functional data. Australian National University. July 27, 2023. Accessed August 08, 2025. https://online.provdatabase.com/cite/5567

APA 7th EditionReferences

Mosler, Karl. (2023). General notions of depth for functional data. Australian National University. https://online.provdatabase.com/cite/5567

Chicago 17th EditionReferences

Mosler, Karl. General notions of depth for functional data. Australian National University. 2023. https://online.provdatabase.com/cite/5567

HarvardReferences

Mosler, Karl. (2023). General notions of depth for functional data. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/5567. (Accessed 08 August 2025).

Harvard AustraliaReferences

Mosler, Karl, 2023, General notions of depth for functional data, Australian National University, viewed 08 August 2025, <https://online.provdatabase.com/cite/5567>

MLA 9th EditionWorks Cited

Mosler, Karl. General notions of depth for functional data. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/5567.

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