Estimation of a discriminant function on the basis of a small sample size from a mixture of two inverse Gaussian distributions is considered. Its performance is investigated by a series of simulation experiments. The relative efilciency of the mixture and classified discrimination procedures are evaluated from the simulation results and compared with available asymptotic relative efficiency results.KEY WORDS: Discriminant function; estimation from mixtures, mixture of inverse Gaussian populations, relative efficiency.
The problem of updating discriminant functions estimated from inverse Gaussian populations is investigated in situations when the additional observations are mixed (unclassified) or classified. In each case two types of discriminant functions, linear and quadratic, are considered. Using simulation experiments the performance of the updating procedures is evaluated by means of relative efficiencies.
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