Robustness of Normal-Based Multi-Stage Sequential Sampling Procedures

摘要

Multistage sequential procedures have been developed to resolve the drawbacks of fixed sample size procedures, to tackle a variety of inference problems, accomplish predetermined optimal criteria and to make operational savings possible by bulk sampling. Although the theory was developed under the normality assumption, several continuous and discrete distribution parameters were investigated within this optimal statistical decision framework. The current study is designed to answer two main questions. First, how sensitive are these sampling procedures to changes in the underlying distributions from normality? Second, how do shifts in parameters affect the measures of the quality of inference? With first question, our investigation focuses on some departure from normality. The second question concerns the sensitivity of both coverage probability and the Type II error probability of the fixed width confidence intervals to detect possible shifts in the true parameters. Monte Carlo simulations were performed in typical situations to study moderate to large-sample-size performances for these departures.