|Student Name:||Captain Aaron Lessin|
|Thesis:||ESTIMATING THE PROBABILITY OF BEING THE BEST SYSTEM:|
|Location:||Bldg 641, Rm 221 (ENC Conference Room)|
|Date & Time:||02/21/2013 at 1230|
|Abstract:|| This thesis provides two new approaches for comparing competing systems. Instead of making comparisons based on long run averages or mean performance, the first paper presents a generalized method for calculating the probability that a single system is the best among all systems in a single trial. Unlike current empirical methods, the generalized method calculates the exact multinomial probability that a single system is best among competing systems. The ability to avoid time consuming empirical estimation techniques could potentially result in significant savings in both time and money when comparing alternate systems. A Monte Carlo simulation study is conducted comparing the empirical probability estimates of the generalized integral method, calculated using density estimation technique, with those of two related estimation techniques, Procedure BEM (Bechhofer, Elmaghraby, and Morse) and Procedure AVC (All Vector Comparisons). All test cases show comparable performance in empirical estimation accuracy of the generalized integral method with that of the current methods analyzed. The second approach proposes the use of a distribution-free ordered comparisons procedure to test whether one population is stochastically larger (or smaller) than the other. This procedure is suggested as a useful alternative to the well-known Wilcoxon rank sum and Mann-Whitney tests.