Outlier Detection in Multivariate and High-Dimensional Datasets
Abstract
Accurate detection of outliers is crucial in the field of statistical analysis. Using classical statistical models 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 for 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.
Subject Area
Statistics|Computer science|Applied Mathematics
Recommended Citation
Wu, Yuanhong, "Outlier Detection in Multivariate and High-Dimensional Datasets" (2023). ETD Collection for University of Texas, El Paso. AAI30494164.
https://scholarworks.utep.edu/dissertations/AAI30494164