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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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Bibliographic Details
Main Author: Hohmann, Daniel
Contributors: Holzmann, Hajo (Prof. Dr.) (Thesis advisor)
Format: Doctoral Thesis
Language:English
Published: Philipps-Universität Marburg 2014
Subjects:
random coefficient model
Mischungsmodell
Radon transform
nonparametric estimation
limited angle problem
Radontransformation
Asymptotic properties
Mischung <Mathematik>
random-coefficient-Modell
Estimation
efficient estimation
asymptotic normality
nichtparametrisches Schätzen
Mathematik
Radon-Transformation
Nichtparametrische Schätzung
limited-angle-Problem
mixture model
asymptotische Normalität
Mathematics
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Description
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.
DOI:10.17192/z2014.0117

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