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On Kersting’s Theorem on Weak Convergence of Recursions

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Mathematical Learning Models — Theory and Algorithms

Part of the book series: Lecture Notes in Statistics ((LNS,volume 20))

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Abstract

In an interesting paper ([1]), G. Kersting considers the asymptotic distribution of a sequence of random variables (Xn)n ∈ ℕ given by the recursion

$$ {x_{{n + 1}}} = {X_{n}} - a_{n}^{2}h\left( {{X_{n}}} \right) + {a_{n}}{Y_{n}} $$
((1))

Such types of recursive schemes are encountered in the framework of Stochastic Approximation. (Kersting derives e.g. with the help of his result the asymptotic distribution of the Robbins-Monro process when the regression function is only one-sided differentiable at the root).

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References

  1. Kersting, G.D. (1978). A weak convergence theorem with application to the Robbins-Monro process. Ann. Prob. Vol. 6, No. 6, 1015–1025

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  2. Loeve, M. (1977). Probability Theory I, 4th ed. Springer Verlag New York Heidelberg Berlin.

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© 1983 Springer-Verlag New York Inc.

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Pflug, G.C. (1983). On Kersting’s Theorem on Weak Convergence of Recursions. 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_18

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  • DOI: https://doi.org/10.1007/978-1-4612-5612-0_18

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-90913-4

  • Online ISBN: 978-1-4612-5612-0

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