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Doctoral thesis
Open access
English

Robust inference for random fields and latent models

Defense date2016-08-26
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.

eng
Citation (ISO format)
MOLINARI, Roberto Carlo. Robust inference for random fields and latent models. 2016. doi: 10.13097/archive-ouverte/unige:86899
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Technical informations

Creation09/01/2016 11:17:00 AM
First validation09/01/2016 11:17:00 AM
Update time03/15/2023 12:42:43 AM
Status update03/15/2023 12:42:43 AM
Last indexation01/29/2024 8:49:20 PM
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