In this thesis, we study indifference-zone multinomial selection procedures, that is, procedures for selecting the most probable ("best") multinomial cell. Such procedures have a number of real-world applications — for instance, which is the most popular television show in a particular time slot, or which manufacturing strategy has the highest probability of yielding the largest profit on a particular trial? The indifference-zone procedures we examine all satisfy a probability requirement that guarantees to correctly select with high probability the best multinomial category under a variety of underlying probability configurations. We show by Monte Carlo and exact calculations that certain sequential sampling procedures perform better than others. We also offer various extensions and thoughts for future research
