246 – 246 - Data Science
A Sequential Discrimination Procedure for Two Almost Identically Shaped Real Distributions
Silvey Shamsi
Ball State University
The way of investigating a distribution knowing its interesting properties might be often inadequate when the shapes of two real distributions are almost similar. In each of these circumstances, the accurate decision about the genesis of a random sample from any of the two parent real distributions will be very much ambiguous even in the presence of the existing testing procedure of the real data. A sequential discrimination procedure has been suggested which is consisting of two tests. It is also invariant to the sample size. The pragmatic performance of the proposed discrimination procedure has been evaluated by checking its meticulous capacity of detecting the genesis of the known samples from the two identically shaped real distributions. Long run simulation studies also show that the proposed test is perfectly correct whereas the individual traditional tests were highly capricious in between the range of 3% to 75%. Further scopes have also been captivated by the proposed tests.