Nonparametric estimation in models for unobservable heterogeneity

Nonparametric estimation in models for unobservable heterogeneity

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 transf...

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Bibliografische gegevens
Hoofdauteur: Hohmann, Daniel
Andere auteurs: Holzmann, Hajo (Prof. Dr.) (Thesis begeleider)
Formaat: Dissertation
Taal:Engels
Gepubliceerd in: Philipps-Universität Marburg 2014
Onderwerpen:
Radontransformation
limited-angle-Problem
Asymptotic properties
asymptotische Normalität
Mischung <Mathematik>
mixture model
random-coefficient-Modell
Estimation
asymptotic normality
Mischungsmodell
Radon transform
limited angle problem
nichtparametrisches Schätzen
efficient estimation
Mathematik
random coefficient model
Radon-Transformation
Nichtparametrische Schätzung
nonparametric estimation
Mathematics
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Plaatsingsnummer: urn:nbn:de:hebis:04-z2014-01176
Publicatiedatum: 2014-03-18
Datum der Annahme: 2014-02-28
Downloads: 40 (2024), 57 (2023), 64 (2022), 85 (2021), 57 (2020), 104 (2019), 56 (2018)
Lizenz: https://rightsstatements.org/vocab/InC-NC/1.0/
URL naar item: https://archiv.ub.uni-marburg.de/diss/z2014/0117
https://doi.org/10.17192/z2014.0117

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