Density estimation from correlated data

This paper evaluates sequential procedures for estimating the steady‐state density of a stochastic process, typically (though not necessarily) observed by simulation, with or without intra‐process independence. The procedure computes sample densities at certain points and uses Lagrange interpolation to estimate the density f(x) for each user‐specified x. The procedure sequentially determines the sample size by an intrinsic quasi‐independent sequence and estimates the density by central finite differences. An experimental performance evaluation demonstrates the validity of using the procedure to estimate densities of steady‐state stochastic processes.