Abstract
Results, obtained earlier, on asymptotic properties of an adaptive RM stochastic approximation method, are strengthened. The method is shown optimal in the sense of local asymptotic minimax risk and robust with respect to small changes of the unknown density and small changes of the estimated parameter.
Research begun during author’s visit at the University of Bern in 1979 and partially supported by NSF grants MCS-7802846 and MCS-8102233.
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© 1983 Springer-Verlag New York Inc.
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Fabian, V. (1983). A Local Asymptotic Minimax Optimality of an Adaptive Robbins Monro Stochastic Approximation Procedure. In: Herkenrath, U., Kalin, D., Vogel, W. (eds) Mathematical Learning Models — Theory and Algorithms. Lecture Notes in Statistics, vol 20. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5612-0_5
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DOI: https://doi.org/10.1007/978-1-4612-5612-0_5
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