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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...
में बचाया:
| मुख्य लेखक: | |
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| अन्य लेखक: | |
| स्वरूप: | Dissertation |
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
Philipps-Universität Marburg
2018
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| विषय: | |
| ऑनलाइन पहुंच: | पीडीएफ पूर्ण पाठ |
| टैग: |
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| सारांश: | 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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| भौतिक वर्णन: | 166 Seiten |
| डिजिटल ऑब्जेक्ट पहचानकर्ता: | 10.17192/z2019.0100 |