Likelihood Ratio Tests for Principal Components
β Scribed by L. Dumbgen
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 425 KB
- Volume
- 52
- Category
- Article
- ISSN
- 0047-259X
No coin nor oath required. For personal study only.
β¦ Synopsis
A particular class of tests for the principal components of a scatter matrix (\Sigma) is proposed. In the simplest case one wants to test whether a given vector is an eigenvector of (\Sigma) corresponding to its largest eigenvalue. The test statistics are likelihood ratio statistics for the classical Wishart model, and critical values are obtained parametrically as well as nonparametrically without making any assumptions on the eigenvalues of (\Sigma). Still, the tests have asymptotic properties similar to those of classical procedures and are asymptotically admissible and optimal in some sense. 1995 Academic Press, Inc.
π SIMILAR VOLUMES
The theoretical principles and practical implementation of a new method for multivariate data analysis, maximum likelihood principal component analysis (MLPCA), are described. MLCPA is an analog to principal component analysis (PCA) that incorporates information about measurement errors to develop P