Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17791
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dc.contributor.authorKazakeviciute, A-
dc.contributor.authorOlivo, M-
dc.date.accessioned2019-03-26T12:29:46Z-
dc.date.available2018-12-19-
dc.date.available2019-03-26T12:29:46Z-
dc.date.issued2018-12-19-
dc.identifierhttps://projecteuclid.org/euclid.ejs/1545188496-
dc.identifier.citationKazakeviciute, A. and Olivo, M. (2018) 'Consistency of logistic classifier in abstract Hilbert spaces', Electronic Journal Statististics, 12 (2), pp. 4487 - 4516. doi: 10.1214/18-EJS1514.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/17791-
dc.description.abstractWe study the asymptotic behavior of the logistic classifier in an abstract Hilbert space and require realistic conditions on the distribution of data for its consistency. The number kn of estimated parameters via maximum quasi-likelihood is allowed to diverge so that kn/n → 0 and nτ 4 kn → ∞, where n is the number of observations and τkn is the variance of the last principal component of data used for estimation. This is the only result on the consistency of the logistic classifier we know so far when the data are assumed to come from a Hilbert space.en_US
dc.format.extent4487 - 4516-
dc.language.isoenen_US
dc.publisherInstitute of Mathematical Statisticsen_US
dc.rightsCopyright for all articles in EJS is CC BY 4.0.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectClassificationen_US
dc.subjectconsistencyen_US
dc.subjectfunctional data analysisen_US
dc.subjectlogistic classifieren_US
dc.titleConsistency of logistic classifier in abstract Hilbert spacesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1214/18-EJS1514-
pubs.volume12-
Appears in Collections:Dept of Mathematics Research Papers

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