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Application of Robust Regression in Body Fat Percentage
Tee Xing Yi
Supervisor: Assoc. Prof. Dr. Norizan Binti Mohamed
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
In regression model, the dataset which contaminated with outliers can bring a big
distortion to Ordinary Least Squares (OLS) estimator and bring an unstable or unreliable
result. An outlier is a point which lies far away from others in a random population sample.
If a model successful to fit the data then it will extremely useful in multiple regression
model. In this research, we introduce a robust regression estimator to detect outliers in
a contaminated dataset and provide a reliable result which has different type of
estimators that can be used in investigating the relationship between two or more
variables such as M estimator, S estimator, MM estimator and LTS estimator. Besides
that, the efficiency of estimators will be analysed and we expect that the robust M
estimation is an alternative approach in dealing with outliers in a real life dataset and
provide a resistant result.
900 | UMT UNDERGRADUATE RESEARCH DAY 2018