A multivariate analogue of a one-sided test is presented for multinomial distributions. The test involves a constrained optimization that is formulated using Karush-Kuhn-Tucker conditions. An explicit solution to the constrained optimization is derived by adapting the water filling algorithm encountered in information theory. Feasibility of the resulting test is demonstrated using standardized anomaly detection tasks from the literature.
