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Outlier Detection by Using Robust Regression Parameter Estimation
Nurul Najwa Binti Abdullah
Supervisor: Mrs Nor Azlida Binti Aleng
Bachelor of Science (Financial Mathematics)
School of Information and Applied Mathematics
The presence of outliers in a data set can result in significant error rates. Outlier that
exists in some data sets can be important data for the data set or may be non-critical
data in a data set. The study was conducted using 500 blood pressure data sample The
purpose of this study was to investigate the presence of outlier in blood pressure data
sample, making comparisons on some of the best methods of detecting outlier and to
test the robustness of sampled data when contaminated by 10%, 30% and 50%. The
method used in this study was a robust regression method using the Least Trimmed
Square (LTS) estimator and the MM estimator. The outlier was detected by using the SAS
9.4. The findings show that outlier detection using robust regression method which is LTS
estimators is more effective than MM estimators.
879 | UMT UNDERGRADUATE RESEARCH DAY 2018