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...
Gespeichert in:
1. Verfasser: | |
---|---|
Beteiligte: | |
Format: | Dissertation |
Sprache: | Englisch |
Veröffentlicht: |
Philipps-Universität Marburg
2018
|
Schlagworte: | |
Online Zugang: | PDF-Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
Umfang: | 166 Seiten |
DOI: | 10.17192/z2019.0100 |