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Application of Robust Regression Model on Mortality Rate Data
Siti Nursuhana Binti Ramli
Supervisor: Assoc. Prof Dr. Norizan Binti Mohamed
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
Human mortality rate caused by diseases, accidents and others that affect the people in
the United States. This research was carried out to add findings of the use of death rate
data in mathematics. The data used is death rate data from USA Life Expectancy.
Mortality rate data in fifty countries in the US are selected based on the several causes
as an independent variable which are diabetes, kidney disease, stroke, accident, lung
disease, Alzheimer and influenza pneumonia. Robust regression methods are introduced
to detect the outliers in the data. In this study, the M, S, MM and LTS of robust regression
estimator were used in this study to make a comparison between estimators and to build
the best model. Then, the data were contaminated to determine the robustness of the
model. The result showed that the model is robust even though the data were
contaminated.
891 | UMT UNDERGRADUATE RESEARCH DAY 2018