On the Robustness of Model-Based Sampling in Auditing.

In contrast with classical, randomization-based strategies, model-based sampling is purposive, i.e., not random in nature. Unfortunately, the optimality of model-robust procedures depends heavily on the assumption that the true form of the superpopulation model is known to the sampler prior to sampling. To date, model-based sampling has not found wide application due to concerns about the robustness of the approach in the presence of superpopulation model misspecification. This paper evaluates the robustness of model-based sampling strategies in audit settngs. In doing so we introduce the concept of model-robust sampling, an extension of model-based sampling which provides some protection against model misspecification. An efficient algorithm for sample selection is presented. Simulation is used to measure the robustness of the various model-based approaches to changes in assumptions about the target population. We conclude that while substantive gains in efficiency are possible through model-based sampling, randomization-based strategies should be preferred in the absence of reliable prior information as to the assumed form of the variance function.