The Kendall theorem and its application to the geometric ergodicity of Markov chains

In this paper we prove an improved quantitative version of the Kendall’s Theorem. The Kendal Theorem states that under mild conditions imposed on a probability distribution on positive integers (i.e. probabilistic sequence) one can prove convergence of its renewal sequence. Due to the well-known property the first entrance last exit decomposition such results are of interest in the stability theory of time homogeneous Markov chains. In particular the approach may be used to measure rates of convergence of geometrically ergodic Markov chains and consequently implies estimates on convergence of MCMC estimators.