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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Vydané v:	Journal of statistical planning and inference Ročník 203; s. 1 - 19
Hlavný autor:	Godichon, Antoine
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2019 Elsevier
Predmet:	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
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