Bayes classification rule for the general discrete case
Pattern Recognition
Monte Carlo comparison of six hierarchical clustering methods on random data
Pattern Recognition
Artificial intelligence and statistics
The use of Monte Carlo methods in natural resource management models
ANSS '86 Proceedings of the 19th annual symposium on Simulation
Performance of clustering algorithms on time-dependent spatial patterns
ANSS '88 Proceedings of the 21st annual symposium on Simulation
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Monte Carlo simulation experiments are used to assess performance of seven models for discrete discriminant analysis. Discriminant rules are obtained for binary observation vectors from training samples drawn from two-group populations. Performance is evaluated primarily in terms of the error rate observed in the training and test samples for populations characterized by small and unequal training sample sizes, log-likelihood reversals, various correlation structures, and missing values. Some models are superior for certain population structures, and an attempt is made to identify data set characteristics that favor a particular model. Applications of the models are discussed, including their use as components in expert systems, and an expert system for consulting on discriminant analysis problems is reviewed.