Estimation of Parameters of Zero-One Processes by Interval Sampling: An Adaptive Strategy.

摘要

In a previous paper, the problem of estimating the parameters was considered giving the mean time in each stage, for a two-stage Poisson process, when sampling was permitted only at equal intervals. It was impossible to get good results unless the intervals were small. Now an adaptive strategy is proposed in which the interval is successively halved until a suitable stage is reached; then all samples can be combined to give estimates. The strategy is examined by Monte Carlo methods, and it is shown to give a considerable improvement over the one-stage method. Figures are given to illustrate the results; they can be used also to improve estimates and give confidence intervals. A technique is proposed to give an approximate confidence ellipse for the two parameters, which appears to work well for the ranges considered.