Gutenberg Open Science: Polynomials under Ornstein–Uhlenbeck noise and an application to inference in stochastic Hodgkin

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5803

Full metadata record

DC Field	Value	Language
dc.contributor.author	Höpfner, Reinhard	-
dc.date.accessioned	2021-05-11T09:14:49Z	-
dc.date.available	2021-05-11T09:14:49Z	-
dc.date.issued	2021	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/5812	-
dc.description.abstract	We discuss estimation problems where a polynomial s→∑ℓi=0ϑisi with strictly positive leading coefficient is observed under Ornstein–Uhlenbeck noise over a long time interval. We prove local asymptotic normality (LAN) and specify asymptotically efficient estimators. We apply this to the following problem: feeding noise dYt into the classical (deterministic) Hodgkin–Huxley model in neuroscience, with Yt=ϑt+Xt and X some Ornstein–Uhlenbeck process with backdriving force τ, we have asymptotically efficient estimators for the pair (ϑ,τ); based on observation of the membrane potential up to time n, the estimate for ϑ converges at rate n3−−−√.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	510 Mathematik	de_DE
dc.subject.ddc	510 Mathematics	en_GB
dc.title	Polynomials under Ornstein–Uhlenbeck noise and an application to inference in stochastic Hodgkin–Huxley systems	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-5803	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Statistical inference for stochastic processes	de
jgu.journal.volume	24	de
jgu.pages.start	35	de
jgu.pages.end	59	de
jgu.publisher.year	2021	-
jgu.publisher.name	Springer Science + Business Media B.V.	de
jgu.publisher.place	Dordrecht	de
jgu.publisher.uri	https://doi.org/10.1007/s11203-020-09226-0	de
jgu.publisher.issn	1572-9311	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	510	de
jgu.publisher.doi	10.1007/s11203-020-09226-0
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

Files in This Item:

	File	Description	Size	Format
	höpfner_reinhard-polynomials_un-20210421200155509.pdf		525.45 kB	Adobe PDF	View/Open

Show simple item record