Abstract
The problem of selecting the population with the largest probability of success from among k(≥ 2) independent Bernoulli populations is investigated. The population to be selected must be as good as or better than a control. It is assumed that past observations are available when the current selection is made. Therefore, the empirical Bayes approach is employed. Combining useful information from the past data, an empirical. Bayes two-stage selection procedure is developed. It is proved that the proposed empirical Bayes two-stage selection procedure is asymptotically optimal, having a rate of convergence of order O(exp(-cn)), for some positive constant c, where n is the number of past observations at hand.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
