Kernel density estimator for strong mixing processes

In this paper, we study the consistency and central limit theorem of the kernel density estimator when the random sample is a realization of a (nonstationary) α-mixing process. It is revealed that the rate of convergence depends upon the degree of dependency and the structure of the underlying densities.