Anderson's classification statistic based on a post-stratified training sample |
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Authors: | CY Leung |
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Institution: | The Chinese University of Hong Kong , Shatin, N.T, Hong Kong |
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Abstract: | The performance of Anderson's classification statistic based on a post-stratified random sample is examined. It is assumed that the training sample is a random sample from a stratified population consisting of two strata with unknown stratum weights. The sample is first segregated into the two strata by post-stratification. The unknown parameters for each of the two populations are then estimated and used in the construction of the plug-in discriminant. Under this procedure, it is shown that additional estimation of the stratum weight will not seriously affect the performance of Anderson's classification statistic. Furthermore, our discriminant enjoys a much higher efficiency than the procedure based on an unclassified sample from a mixture of normals investigated by Ganesalingam and McLachlan (1978). |
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Keywords: | Anderson's classification statistic post-stratified training sample mixture of normal populations stratum weight probabilities of misclassification overall error of joisclassification asymptotic efficiency |
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