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...
שמור ב:
| מחבר ראשי: | |
|---|---|
| מחברים אחרים: | |
| פורמט: | Dissertation |
| שפה: | אנגלית |
| יצא לאור: |
Philipps-Universität Marburg
2018
|
| נושאים: | |
| גישה מקוונת: | PDF-Volltext |
| תגים: |
הוספת תג
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
| סיכום: | 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. |
|---|---|
| תיאור פיזי: | 166 Seiten |
| DOI: | 10.17192/z2019.0100 |