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Outlier Detection Using the Relative Range Distribution
Dallah, Dania
Dallah, Dania
Description
A Master of Science in Mathematics by Dania Dallah entitled, “Outlier Detection Using the Relative Range Distribution”, submitted in July 2024. Thesis advisor is Dr. Hana Sulieman and thesis co-advisor is Dr. Ayman Alzaatreh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
Outlier detection plays a crucial role in data analysis. Outlier detection is a challenging task due to the subjective nature of defining what constitutes an outlier. By identifying and appropriately handling outliers, analysts can gain a deeper understanding of the data, improve the quality of analyses, and make more informed decisions. In this thesis, we propose a new measure for detecting outliers in univariate data. The new measure, called relative range, is defined as the range statistic divided by the interquartile range (IQR). Since the range provides a simple yet effective measure of data dispersion, analyzing the range distribution will help identify potential outliers that fall outside the expected range of values. The probability distribution of the relative range is estimated for both symmetrical and skewed data distributions using Monte Carlo simulations. Based on the estimated empirical distribution of the relative range, a threshold is determined and used to detect potential outliers. The thesis also proposes a sequential approach for outlier detection based on the relative range. In general, the relative range has shown to be a more robust statistic at detecting outliers in both sequential and non-sequential outlier detection.