## ABSTRACT In this paper, we investigate the performance of a class of Mβestimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of valueβatβrisk. The class of estimators includes the least absolute deviation (LAD), Huber's, Cauchy and Bβestimator, as well
β¦ LIBER β¦
Generalization performance of -support vector classifier based on conditional value-at-risk minimization
β Scribed by Akiko Takeda
- Book ID
- 113816186
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 372 KB
- Volume
- 72
- Category
- Article
- ISSN
- 0925-2312
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