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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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Contributor : Alexandre Renaux Connect in order to contact the contributor
Submitted on : Tuesday, July 19, 2016 - 12:41:50 PM
Last modification on : Friday, October 7, 2022 - 4:00:14 AM


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Jérôme Galy, Eric Chaumette, François Vincent, Alexandre Renaux, Pascal Larzabal. Lower bounds for non-standard deterministic estimation. SAM: Sensor Array and Multichannel Signal Processing, Jul 2016, Rio de Janeiro, Brazil. ⟨10.1109/sam.2016.7569710⟩. ⟨hal-01346613⟩



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