  Stochastic differential equations and stochastic dynamics are good models to
describe stochastic phenomena in real world. In this paper, we study N
independent stochastic processes Xi(t) with real entries and the processes are
determined by the stochastic differential equations with drift term relying on
some random effects. We obtain the Girsanov-type formula of the stochastic
differential equation driven by Fractional Brownian Motion through kernel
transformation. Under some assumptions of the random effect, we estimate the
parameter estimators by the maximum likelihood estimation and give some
numerical simulations for the discrete observations. Results show that for the
different H, the parameter estimator is closer to the true value as the amount
of data increases.
