Publikationsserver
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...
Sparad:
| Huvudupphovsman: | |
|---|---|
| Övriga upphovsmän: | |
| Materialtyp: | Dissertation |
| Språk: | engelska |
| Publicerad: |
Philipps-Universität Marburg
2018
|
| Ämnen: | |
| Länkar: | PDF-fulltext |
| Taggar: |
Lägg till en tagg
Inga taggar, Lägg till första taggen!
|
| Sammanfattning: | 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. |
|---|---|
| Fysisk beskrivning: | 166 Seiten |
| DOI: | 10.17192/z2019.0100 |