UNIGE document Doctoral Thesis
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Robust inference for random fields and latent models

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Defense Thèse de doctorat : Univ. Genève, 2016 - GSEM 32 - 2016/08/26
Abstract This thesis delivers a new framework for the robust parametric estimation of random fields and latent models through the use of the wavelet variance. By proposing a new M-estimation approach for the latter quantity and delivering results on the identifiability of a wide class of latent models, the thesis finally delivers a computationally efficient and statistically sound method to estimate complex models even when the data is contaminated. The results of this work are then implemented within a new statistical software which is also presented in this thesis, with a focus on its usefulness for inertial sensor calibration.
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URN: urn:nbn:ch:unige-868996
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MOLINARI, Roberto Carlo. Robust inference for random fields and latent models. Université de Genève. Thèse, 2016. https://archive-ouverte.unige.ch/unige:86899

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Deposited on : 2016-09-12

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