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...
محفوظ في:
المؤلف الرئيسي: | |
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مؤلفون آخرون: | |
التنسيق: | Dissertation |
اللغة: | الإنجليزية |
منشور في: |
Philipps-Universität Marburg
2018
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الموضوعات: | |
الوصول للمادة أونلاين: | PDF النص الكامل |
الوسوم: |
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الملخص: | 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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وصف مادي: | 166 Seiten |
DOI: | 10.17192/z2019.0100 |