Adaptive Designs for Optimal Observed Fisher Information

Abstract

Expected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast to the common claim that the inverse of observed Fisher information is a better approximation to the variance of the maximum likelihood estimator. Observed Fisher information cannot be known a priori; however, if an experiment is conducted sequentially (in a series of runs) the observed Fisher information from previous runs is available. In the current work two adaptive designs are proposed that use the observed Fisher information from previous runs in the design of the current run.