Poisson processes with nearly constant failure intensity

Poisson processes with failure intensity functions that are approximately constant are investigated. Maximum likelihood estimation procedures are used to estimate the failure intensity for a fixed level of approximation. The level of approximation is chosen through an adaptation of crossvalidation. Since model performance changes with time, the authors utilize a procedure that adaptively selects models over time. The predictive performance of such an adaptive procedure based on selection from among three nonparametric models is compared to that of the logarithmic Poisson and the exponential Poisson models for two software reliability data sets.<>