Statistical Identification in Multinomial Models with Sequential Sampling

SYNOPTIC ABSTRACT

We propose an inverse-type sequential method of statistical identification in multinomial models having unequal cell probabilities. Using the indifference-zone formulation and based on the likelihood ratio of decision vectors, a stopping rule is devised that controls the probability of a correct identification, P {CI} and satisfies a preassigned probability level condition P*. By performing a Monte Carlo experiment, the expected sample sizes are obtained and the numerical results of the proposed procedure are presented for illustration.

Key Words and Phrases:

• statistical identification
• inverse sequential sampling
• indifference-zone formulation
• decision vectors
• probability of correct identification