Publikationsserver der Universitätsbibliothek Marburg
Titel:Nonparametric estimation in models for unobservable heterogeneity
Autor:Hohmann, Daniel
Weitere Beteiligte: Holzmann, Hajo (Prof. Dr.)
Veröffentlicht:2014
URI:https://archiv.ub.uni-marburg.de/diss/z2014/0117
DOI: https://doi.org/10.17192/z2014.0117
URN: urn:nbn:de:hebis:04-z2014-01176
DDC: Mathematik
Titel (trans.):Nichtparametrisches Schätzen in Modellen für nichtbeobachtbare Heterogenität
Publikationsdatum:2014-03-18
Lizenz:https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Radontransformation, limited-angle-Problem, asymptotische Normalität, Mischung <Mathematik>, mixture model, random-coefficient-Modell, asymptotic normality, Mischungsmodell, Radon transform, limited angle problem, nichtparametrisches Schätzen, efficient estimation, random coefficient model, Radon-Transformation, Nichtparametrische Schätzung, nonparametric estimation

Summary:
Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.

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