Asymptotic efficiency of a stochastic approximation algorithm is proved under weak conditions: no growth rate restriction is imposed on the regression function; wide class of observation noises is treated. The algorithm considered in the paper is characterized by slow gain, truncations at randomly varying bounds and averaging.
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Han-Fu Chen (1993) studied this question.
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