Date of Award
2023-05-01
Degree Name
Master of Science
Department
Statistics
Advisor(s)
Michael M. Pokojovy
Abstract
Accurate detection of outliers is crucial in the field of statistical analysis. Using classical statisticalmodels without considering the presence of outliers in the data can lead to misleading outcomes. There exist a myriad of procedures to detect outliers in statistics. We concentrate on the statistical techniques that can robustly identify outliers in data sets. To this end, we pursue two aims. First, we give an extensive overview of robust statistical methods which are still popular in recent years for outlier detection. We provide the definitions, algorithms and also discuss some important properties of these methods. Second, two real examples are presented to make a comparison between several techniques. Three prevalent methods are selected to illustrate their practical use for outlier detection in both low-dimensional and high-dimensional data.
Language
en
Provenance
Recieved from ProQuest
Copyright Date
2023-05-01
File Size
p.
File Format
application/pdf
Rights Holder
Yuanhong Wu
Recommended Citation
Wu, Yuanhong, "Outlier Detection In Multivariate And High-Dimensional Datasets" (2023). Open Access Theses & Dissertations. 3872.
https://scholarworks.utep.edu/open_etd/3872