Lower bounds for non-standard deterministic estimation

Abstract : In this paper, non standard deterministic parameters estimation is considered, i.e. the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on additional random variables. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using the known deterministic lower bounds on the mean-squared-error (MSE). However an embedding mechanism allows to transpose all the known lowers bounds into modified lower bounds fitted with non-standard deterministic estimation, encompassing the modified Cramér-Rao / Bhattacharyya bounds and hybrid lower bounds.
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Communication dans un congrès
9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016), Jul 2016, Rio de Janeiro, Brazil. SAM 2016. 〈10.1109/sam.2016.7569710 〉
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Soumis le : mardi 19 juillet 2016 - 12:41:50
Dernière modification le : samedi 5 mai 2018 - 09:42:03

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Jérôme Galy, Eric Chaumette, François Vincent, Alexandre Renaux, Pascal Larzabal. Lower bounds for non-standard deterministic estimation. 9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016), Jul 2016, Rio de Janeiro, Brazil. SAM 2016. 〈10.1109/sam.2016.7569710 〉. 〈hal-01346613〉

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