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Linear discrimination for multi-level multivariate data with separable means and jointly equicorrelated covariance structure
- Source :
- Journal of Statistical Planning and Inference. 141:1910-1924
- Publication Year :
- 2011
- Publisher :
- Elsevier BV, 2011.
-
Abstract
- In this article we study a linear discriminant function of multiple m -variate observations at u -sites and over v -time points under the assumption of multivariate normality. We assume that the m -variate observations have a separable mean vector structure and a “jointly equicorrelated covariance” structure. The new discriminant function is very effective in discriminating individuals in a small sample scenario. No closed-form expression exists for the maximum likelihood estimates of the unknown population parameters, and their direct computation is nontrivial. An iterative algorithm is proposed to calculate the maximum likelihood estimates of these unknown parameters. A discriminant function is also developed for unstructured mean vectors. The new discriminant functions are applied to simulated data sets as well as to a real data set. Results illustrating the benefits of the new classification methods over the traditional one are presented.
- Subjects :
- Statistics and Probability
Multiple discriminant analysis
education.field_of_study
Applied Mathematics
Population
Multivariate normal distribution
Covariance
Linear discriminant analysis
Discriminant
Discriminant function analysis
Optimal discriminant analysis
Statistics
Statistics, Probability and Uncertainty
education
Mathematics
Subjects
Details
- ISSN :
- 03783758
- Volume :
- 141
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Planning and Inference
- Accession number :
- edsair.doi...........92c0012313ac1d7847f7d9ad1798a3a1
- Full Text :
- https://doi.org/10.1016/j.jspi.2010.12.001