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Maximal autocorrelation functions in functional data analysis

Hooker, Giles; Roberts, Steven

Description

This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.

dc.contributor.authorHooker, Giles
dc.contributor.authorRoberts, Steven
dc.date.accessioned2016-02-24T22:41:02Z
dc.identifier.issn0960-3174
dc.identifier.urihttp://hdl.handle.net/1885/98538
dc.description.abstractThis paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.
dc.publisherKluwer Academic Publishers
dc.sourceStatistics and Computing
dc.titleMaximal autocorrelation functions in functional data analysis
dc.typeJournal article
local.description.notesImported from ARIES
dc.date.issued2015
local.identifier.absfor010406 - Stochastic Analysis and Modelling
local.identifier.ariespublicationU3488905xPUB5756
local.type.statusPublished Version
local.contributor.affiliationHooker, Giles, Cornell University
local.contributor.affiliationRoberts, Steven, College of Business and Economics, ANU
local.description.embargo2037-12-31
local.identifier.doi10.1007/s11222-015-9582-5
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
dc.date.updated2016-02-24T10:08:46Z
local.identifier.scopusID2-s2.0-84930883516
CollectionsANU Research Publications

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