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Small population size and large dimension performance of some equal mean discrimination functions
Abstract
In this study we consider the problem of classifying a new observation into one of the known groups (pi; i = 1;2) independently distributed multivariate normal when both groups are described by equal mean vectors. The small sample size and large number of parameters performance of four equal mean discriminant functions (Bartlett and Please method (BPM), Bayesian Posterior Probability Approach (BPPA), Quadratic Discriminant Function (QDF) and Absolute Euclidean Distance Classifier (AEDC) were evaluated in classifying observations from two N(mi;Si), p = 10 groups with m1 = m2. The performance evaluation was based on simulated data using reported Balanced and Cross Validation error rates. The BPPA outperformed the other functions. Female liked sex twins data extracted from Stocks (1933) twin data was used for data analysis.
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