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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Главный автор: Werner, Heiko
Другие авторы: Holzmann, Hajo (Prof. Dr.) (Научный руководитель)
Формат: Dissertation
Язык:английский
Опубликовано: Philipps-Universität Marburg 2018
Предметы:
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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Описание
Итог: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.
Объем:166 Seiten
DOI:10.17192/z2019.0100

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