Moderate deviation principles for recursive regression estimators defined by stochastic approximation method
In this paper we prove moderate deviations principles for the recursive kernel estimators of a regression function defined by the stochastic approximation algorithm introduced by R´ev´esz [1973. Robbins-Monro procedure in a Hilbert space and its application in the theory of learning processes I. Studia Sci. Math. Hung., 8, 391-398] and studied by Revesz (1977) and extended by Mokkadem et al., (2009).
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