| Autor: |
Pang, Yik Siong, Ahad, Nor Aishah, Yahaya, Sharipah Soaad Syed, Abdullah, Suhaida |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2896 Issue 1, p1-7, 7p |
| Abstrakt: |
Multivariate data may be contaminated by cellwise and/or casewise outliers. Cellwise outliers are individual data points within a variable that are extreme whereas casewise outliers are observations that come from a different distribution. Similar to other parametric methods, the Classical Multiple Discriminant Rule (CMDR) achieve optimal performance only when the normality assumption is fulfilled. The coexistence of cellwise-casewise outliers can disrupt the data distribution of the sample. Thus, in order to alleviate the problem, this paper employed a distribution-free estimator, Harrell-Davis Median (θ̂HD), together with Robust Covariance (SR) to construct Robust MDR (RMDRHD). The MDRs were evaluated based on misclassification rate via simulation study. The simulation results show that RMDRHD is able to achieve consistently lower misclassification rate than CMDR. Overall, the findings confirmed that the use of the distribution-free θ̂HD to robustify MDR is practical when dealing with both cellwise and casewise outliers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
