Obálka

Uniform convergence rates and uniform adaptive estimation in mixtures of regressions

In this thesis, we develop theoretical tools to examine estimators in non-parametric regression models in regard of uniform convergence rates and uniform adaptivity with respect to the smoothness of the parameter functions. Subsequently, those are applied to non-parametric regression models with Höl...

Celý popis

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Hlavní autor: Werner, Heiko
Další autoři: Holzmann, Hajo (Prof. Dr.) (Vedoucí práce)
Médium: Dissertation
Jazyk:angličtina
Vydáno: Philipps-Universität Marburg 2018
Témata:
mixtures of normals
Gleichmäßige Konvergenzraten
Adaptive Schätzung
minimum contrast estimation
M-estimation
mixtures of regressions
Mischungen von Normalverteilungen
adaptive estimation
M-Schätzung
Mathematik
Mischregressionen
non-parametri
Minimum Kontrast Schätzung
uniform convergence rates
Mathematics
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Popis
Shrnutí:In this thesis, we develop theoretical tools to examine estimators in non-parametric regression models in regard of uniform convergence rates and uniform adaptivity with respect to the smoothness of the parameter functions. Subsequently, those are applied to non-parametric regression models with Hölder-smooth parameter functions. One model is a mixture of Gaussian regressions and the other model is a mixture model with two components and an unspecified symmetric error distribution.
Fyzický popis:166 Seiten
DOI:10.17192/z2019.0100

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