Robust Estimators for the Correlation Measure to Resist Outliers in Data
| Main Author: | Sinsomboonthong, Juthaphorn; Department of Statistics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand |
|---|---|
| Other Authors: | Kasetsart University Research and Development Institute (KURDI) |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
ITB Journal Publisher, LPPM ITB
, 2016
|
| Subjects: | |
| Online Access: |
http://journals.itb.ac.id/index.php/jmfs/article/view/2552 http://journals.itb.ac.id/index.php/jmfs/article/view/2552/2183 http://journals.itb.ac.id/index.php/jmfs/article/downloadSuppFile/2552/686 http://journals.itb.ac.id/index.php/jmfs/article/downloadSuppFile/2552/687 http://journals.itb.ac.id/index.php/jmfs/article/downloadSuppFile/2552/755 |
Daftar Isi:
- The objective of this research was to propose a composite correlation coefficient to estimate the rank correlation coefficient of two variables. A simulation study was conducted using 228 situations for a bivariate normal distribution to compare the robustness properties of the proposed rank correlation coefficient with three estimators, namely, Spearman’s rho, Kendall’s tau and Plantagenet’s correlation coefficients when the data were contaminated with outliers. In both cases of non-outliers and outliers in the data, it was found that the composite correlation coefficient seemed to be the most robust estimator for all sample sizes, whatever the level of the correlation coefficient.