  The goal of this paper is to indicate a new method for constructing normal
confidence intervals for the mean, when the data is coming from stochastic
structures with possibly long memory, especially when the dependence structure
is not known or even the existence of the density function. More precisely we
introduce a random smoothing suggested by the kernel estimators for the
regression function. Applications are presented to linear processes and
reversible Markov chains with long memory.
