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 | |