Uniform convergence rates and uniform adaptive estimation in mixtures of regressions

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...

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Збережено в:
Бібліографічні деталі
Автор: Werner, Heiko
Інші автори: Holzmann, Hajo (Prof. Dr.) (Керівник дипломної роботи)
Формат: Dissertation
Мова:англійська
Опубліковано: Philipps-Universität Marburg 2018
Предмети:
mixtures of normals
Minimum Kontrast Schätzung
mixtures of regressions
Mischungen von Normalverteilungen
adaptive estimation
non-parametri
Gleichmäßige Konvergenzraten
M-estimation
minimum contrast estimation
Mathematik
M-Schätzung
uniform convergence rates
Adaptive Schätzung
Mischregressionen
Mathematics
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Резюме: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.
Фізичний опис:166 Seiten
DOI:10.17192/z2019.0100

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