Online estimation of the asymptotic variance for averaged stochastic gradient algorithms

Stochastic gradient algorithms are more and more studied since they can deal efficiently and online with large samples in high dimensional spaces. In this paper, we first establish a Central Limit Theorem for these estimates as well as for their averaged version in general Hilbert spaces. Moreover,...

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Bibliographic Details

Published in:	Journal of statistical planning and inference Vol. 203; pp. 1 - 19
Main Author:	Godichon, Antoine
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2019 Elsevier
Subjects:	Asymptotic variance Averaging Central Limit Theorem Statistics Statistics Theory Stochastic gradient algorithm Averaging Stochastic gradient algorithm Central Limit Theorem Asymptotic variance
ISSN:	0378-3758, 1873-1171
Online Access:	Get full text
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