DE- and EDP$_{M}$- compound optimality for the information and probability-based criteria

Several optimality criteria have been considered in the literature as information-based criteria. The probability- based criteria have been recently proposed for maximizing the probability of a desired outcome. However, designs that are optimal for the information- based criteria may be inadequate for probability- based criteria. This paper introduces the DE- and EDP${}_{M}$ -- optimum designs for multi aims of optimality for Generalized Linear Models insert ignore into journalissuearticles values(GLMs);. An equivalence theorem is proved for both compound criteria. Finally, two numerical examples are given to illustrate the potentiality of the proposed compound criteria.