Weak convergence of a sequence of stochastic difference equations to a stochastic ordinary differential equation
We consider a sequence of discrete-parameter stochastic processes defined by solutions of stochastic difference equations. A condition is given that this sequence converges weakly to a continuous-parameter process defined by solutions to a stochastic ordinary differential equation. Applying this result, two limit theorems related to population biology are proved. Random parameters in stochastic difference equations are autocorrelated stationary Gaussian processes in the first case. They are jump-type Markov processes in the second case. We discuss a problem of continuous-time approximations for discrete-time models in random environments.
solutions of stochastic difference equations
discrete-time models in random environments
scientific article; zbMATH DE number 4046802