High Breakdown Estimation for Multiple Populations with Applications to Discriminant Analysis
β Scribed by Xuming He; Wing K Fung
- Book ID
- 102602076
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 143 KB
- Volume
- 72
- Category
- Article
- ISSN
- 0047-259X
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β¦ Synopsis
We consider S-estimators of multivariate location and common dispersion matrix in multiple populations. Instead of averaging the robust estimates of the individual covariance matrices, as used by Todorov, Neykov and Neytchev (1990), the observations are pooled for estimating the common covariance more efficiently. Two such proposals are evaluated by a breakdown point analysis and Monte Carlo simulations. Their applications to the discriminant analysis are also considered.
π SIMILAR VOLUMES
Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component analysis (PCA) is a common used dimension reduction technique. Linear discriminate analysis (LDA) is often employed for classification. PCA plus LDA is a famous