Density estimation from correlated data

Abstract

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.