ISSN:
1432-1343
Keywords:
Common principal components
;
Linear Discriminant Function
;
Monte Carlo Simulation
;
Proportional Covariance Matrices
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
Notes:
Abstract In multivariate discrimination of several normal populations, the optimal classification procedure is based on quadratic discriminant functions. We compare expected error rates of the quadratic classification procedure if the covariance matrices are estimated under the following four models: (i) arbitrary covariance matrices, (ii) common principal components, (iii) proportional covariance matrices, and (iv) identical covariance matrices. Using Monte Carlo simulation to estimate expected error rates, we study the performance of the four discrimination procedures for five different parameter setups corresponding to “standard” situations that have been used in the literature. The procedures are examined for sample sizes ranging from 10 to 60, and for two to four groups. Our results quantify the extent to which a parsimonious method reduces error rates, and demonstrate that choosing a simple method of discrimination is often beneficial even if the underlying model assumptions are wrong.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01201025
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