Point separation in logistic regression on Hilbert space-valued variables

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17908

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DC Field	Value	Language
dc.contributor.author	Kazakevičiūtė, A	-
dc.contributor.author	Olivo, M	-
dc.date.accessioned	2019-04-10T15:07:44Z	-
dc.date.available	2017-09-01	-
dc.date.available	2019-04-10T15:07:44Z	-
dc.date.issued	2017-05	-
dc.identifier.citation	Statistics and Probability Letters, 2017, 128 pp. 84 - 88	en_US
dc.identifier.issn	0167-7152	-
dc.identifier.issn	http://dx.doi.org/10.1016/j.spl.2017.04.019	-
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/17908	-
dc.description.abstract	© 2017 Elsevier B.V. We study point separation for the logistic regression model for Hilbert space-valued variables. We prove that the separating hyperplane can be found from a finite set of candidates and give an upper bound for the probability of point separation.	en_US
dc.format.extent	84 - 88	-
dc.language.iso	en	en_US
dc.publisher	Elsevier	en_US
dc.title	Point separation in logistic regression on Hilbert space-valued variables	en_US
dc.type	Article	en_US
dc.identifier.doi	http://dx.doi.org/10.1016/j.spl.2017.04.019	-
dc.relation.isPartOf	Statistics and Probability Letters	-
pubs.publication-status	Published	-
pubs.volume	128	-
Appears in Collections:	Dept of Mechanical and Aerospace Engineering Research Papers

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