Large deviations for distributions of sums of random variables: Markov chain method

Large deviations for distributions of sums of random variables: Markov chain method Let {ξ k } k=0 ∞ be a sequence of i.i.d. real-valued random variables, and let g(x) be a continuous positive function. Under rather general conditions, we prove results on sharp asymptotics of the probabilities $$ P\left\{ {\frac{1} {n}\sum\limits_{k = 0}^{n - 1} {g\left( {\xi _k } \right) < d} } \right\} $$ , n → ∞, and also of their conditional versions. The results are obtained using a new method developed in the paper, namely, the Laplace method for sojourn times of discrete-time Markov chains. We consider two examples: standard Gaussian random variables with g(x) = |x| p , p > 0, and exponential random variables with g(x) = x for x ≥ 0. Problems of Information Transmission Springer Journals

Large deviations for distributions of sums of random variables: Markov chain method

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SP MAIK Nauka/Interperiodica
Copyright © 2010 by Pleiades Publishing, Ltd.
Engineering; Systems Theory, Control; Information Storage and Retrieval; Electrical Engineering; Communications Engineering, Networks
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