A new rate of convergence estimate for homogeneous discrete-time nonlinear Markov chains

Abstract In the paper, we study a new rate of convergence estimate for homogeneous discrete-time nonlinear Markov chains based on the Markov–Dobrushin condition. This result generalizes the convergence estimates for any positive number of transition steps. An example of a class of processes is provided to point that such estimates considering several transition steps may be applicable when one transition can not guarantee any convergence. Moreover, a better estimate can be obtained for a higher number of transitions steps. A law of large numbers is presented for a class of ergodic nonlinear Markov chains with finite state space that may serve as a basis for nonparametric estimation and other statistics.