Pareto set estimation with guaranteed probability of correct selection

Author(s):  
Sigrún Andradóttir ◽  
Judy S. Lee
Author(s):  
Saad T. Bakir

A procedure is developed for selecting a subset which is asserted to contain the “best” of several multinomial populations with a pre-assigned probability of correct selection. According to a pre-chosen linear combination of the multinomial cell probabilities, the “best” population is defined to be the one with the highest such linear combination. As an illustration, the proposed procedure is applied to data relating to the economics of happiness and population income mobility.


Author(s):  
Demet Batur ◽  
F. Fred Choobineh

A value-at-risk, or quantile, is widely used as an appropriate investment selection measure for risk-conscious decision makers. We present two quantile-based sequential procedures—with and without consideration of equivalency between alternatives—for selecting the best alternative from a set of simulated alternatives. These procedures asymptotically guarantee a user-defined target probability of correct selection within a prespecified indifference zone. Experimental results demonstrate the trade-off between the indifference-zone size and the number of simulation iterations needed to render a correct selection while satisfying a desired probability of correct selection.


Sign in / Sign up

Export Citation Format

Share Document