Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains

Details Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains

2011-10-17

arxiv.org/abs/1110.3603v2

Mathematics

Probability

Scientific paper

Let $(S_n)_n$ be a $R^d$-valued random walk ($d\geq2$). Using Babillot's method [2], we give general conditions on the characteristic function of $S_n$ under which $(S_n)_n$ satisfies the same renewal theorem as the classical one obtained for random walks with i.i.d. non-centered increments. This statement is applied to additive functionals of strongly ergodic Markov chains under the non-lattice condition and (almost) optimal moment conditions.

Affiliated with

Also associated with

No associations

Rating

Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multidimensional renewal theory in the non-centered case. Application to strongly ergodic Markov chains will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-317434