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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Veröffentlicht in:	Journal of statistical planning and inference Jg. 203; S. 1 - 19
1. Verfasser:	Godichon, Antoine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.12.2019 Elsevier
Schlagworte:	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-Zugang:	Volltext
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