Dissertation/Thesis Abstract

Contributions to outlier detection methods: Some theory and applications
by Dovoedo, Yinaze Herve, Ph.D., The University of Alabama, 2011, 192; 3478576
Abstract (Summary)

Tukey's traditional boxplot (Tukey, 1977) is a widely used Exploratory Data Analysis (EDA) tools often used for outlier detection with univariate data. In this dissertation, a modification of Tukey's boxplot is proposed in which the probability of at least one false alarm is controlled, as in Sim et al. 2005. The exact expression for that probability is derived and is used to find the fence constants, for observations from any specified location-scale distribution. The proposed procedure is compared with that of Sim et al., 2005 in a simulation study.

Outlier detection and control charting are closely related. Using the preceding procedure, one- and two-sided boxplot-based Phase I control charts for individual observations are proposed for data from an exponential distribution, while controlling the overall false alarm rate. The proposed charts are compared with the charts by Jones and Champ, 2002, in a simulation study.

Sometimes, the practitioner is unable or unwilling to make an assumption about the form of the underlying distribution but is confident that the distribution is skewed. In that case, it is well documented that the application of Tukey's boxplot for outlier detection results in increased number of false alarms. To this end, in this dissertation, a modification of the so-called adjusted boxplot for skewed distributions by Hubert and Vandervieren, 2008, is proposed. The proposed procedure is compared to the adjusted boxplot and Tukey's procedure in a simulation study.

In practice, the data are often multivariate. The concept of a (statistical) depth (or equivalently outlyingness) function provides a natural, nonparametric, "center-outward" ordering of a multivariate data point with respect to data cloud. The deeper a point, the less outlying it is. It is then natural to use some outlyingness functions as outlier identifiers. A simulation study is performed to compare the outlier detection capabilities of selected outlyingness functions available in the literature for multivariate skewed data. Recommendations are provided.

Indexing (document details)
Advisor: Chakraborti, Subhabrata
Commitee: Adams, Benjamin M., Barrett, Bruce, D'Souza, Giles, Lee, Junsoo
School: The University of Alabama
Department: Applied Statistics
School Location: United States -- Alabama
Source: DAI-B 73/02, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Statistics
Keywords: Boxplots, Control charts, Multivariate skewed data, Outlier detection, Outlyingness functions, Statistical process control
Publication Number: 3478576
ISBN: 9781124963013
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest