Inference in inhomogeneous hidden Markov models with application to ion channel data

dc.contributor.advisor	Munk, Axel Prof. Dr.
dc.contributor.author	Diehn, Manuel
dc.date.accessioned	2017-12-18T11:07:51Z
dc.date.available	2017-12-18T11:07:51Z
dc.date.issued	2017-12-18
dc.identifier.uri	http://hdl.handle.net/11858/00-1735-0000-0023-3FB4-2
dc.identifier.uri	http://dx.doi.org/10.53846/goediss-6643
dc.language.iso	eng	de
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc	510	de
dc.title	Inference in inhomogeneous hidden Markov models with application to ion channel data	de
dc.type	doctoralThesis	de
dc.contributor.referee	Munk, Axel Prof. Dr.
dc.date.examination	2017-11-01
dc.description.abstracteng	Ion channel recordings under a changing environment are hardly analyzed and are the main cause for the new model class we introduce. This thesis mainly concerns hidden Markov models with a homogeneous hidden Markov chain and an inhomogeneous observation law, varying in time, but converging to a distribution. The main contribution of this thesis concerns the asymptotic behavior of a quasi-maximum likelihood estimator. In particular, strong consistency and asymptotic normality of this estimator are proven.	de
dc.contributor.coReferee	Rudolf, Daniel J. Prof. Dr.
dc.subject.eng	Hidden Markov Models	de
dc.subject.eng	Inhomogeneous	de
dc.subject.eng	Strong Consistency	de
dc.identifier.urn	urn:nbn:de:gbv:7-11858/00-1735-0000-0023-3FB4-2-1
dc.affiliation.institute	Fakultät für Mathematik und Informatik	de
dc.subject.gokfull	Mathematics (PPN61756535X)	de
dc.identifier.ppn	1009326120

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