Robust Estimators for the Correlation Measure to Resist Outliers in Data

被引：1
|
作者：
Sinsomboonthong, Juthaphorn [1 ]
机构：
[1] Kasetsart Univ, Fac Sci, Dept Stat, Bangkok 10900, Thailand
来源：
JOURNAL OF MATHEMATICAL AND FUNDAMENTAL SCIENCES | 2016年 / 48卷 / 03期
关键词：
correlation coefficient; rank correlation coefficient; outliers; robustness; estimator;
D O I：
10.5614/j.math.fund.sci.2016.48.3.7
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页码：263 / 275
页数：13
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