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...
Furkejuvvon:
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| Eará dahkkit: | |
| Materiálatiipa: | Dissertation |
| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
Philipps-Universität Marburg
2018
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| Fáttát: | |
| Liŋkkat: | PDF-ollesdeaksta |
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| Čoahkkáigeassu: | 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. |
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| Olgguldas hápmi: | 166 Seiten |
| DOI: | 10.17192/z2019.0100 |