Uniform convergence rates and uniform adaptive estimation in mixtures of regressions

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...

Täydet tiedot

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Bibliografiset tiedot
Päätekijä: Werner, Heiko
Muut tekijät: Holzmann, Hajo (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Aineistotyyppi: Dissertation
Kieli:englanti
Julkaistu: Philipps-Universität Marburg 2018
Aiheet:
mixtures of normals
Minimum Kontrast Schätzung
mixtures of regressions
Mischungen von Normalverteilungen
adaptive estimation
non-parametri
Gleichmäßige Konvergenzraten
M-estimation
minimum contrast estimation
Mathematik
M-Schätzung
uniform convergence rates
Adaptive Schätzung
Mischregressionen
Mathematics
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Kuvaus
Yhteenveto: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.
Ulkoasu:166 Seiten
DOI:10.17192/z2019.0100

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