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Activity Number: 635 - Advances in Machine Learning
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330302
Title: Inverse Sampling for Hypothesis Testing of Multinomial Models
Author(s): Hokwon Cho*
Companies: University of Nevada, Las Vegas
Keywords: inverse sampling; testing k-multinomial model; decision-theoretic approach; Dirichlet incomplete integrals; Probability of correct decision; Wheel of fortune

Inverse sampling procedures is considered to test k-variate multinomial models under multiple-decision theoretic point of view. A stopping rule is devised to satisfy a pre-determined P*-condition and to obtain corresponding sample sizes, whereas the Chi-squared test cannot provide. For developing the procedure, the incomplete Dirichlet integrals are primarily used to express the probability of correct decision, P{CD} for various configuration in multinomial models. It is also assumed that the probabilities of all k categories or cells (e.g., on a wheel of fortune) are at least ?(>0). For numerical studies Monte Carlo experiments are conducted, and for an illustration moderate cell configurations of a wheel of fortune will be considered as well.

Authors who are presenting talks have a * after their name.

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