New functional depths and applications
Abstract
Details
- Title: Subtitle
- New functional depths and applications
- Creators
- Xudong Zhang
- Contributors
- Yong Chen (Advisor)Stephen Baek (Committee Member)Matthew Bognar (Committee Member)Andrew Kusiak (Committee Member)Xuan Song (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Industrial Engineering
- Date degree season
- Spring 2020
- Publisher
- University of Iowa
- DOI
- 10.17077/etd.005379
- Number of pages
- xiii, 126 pages
- Copyright
- Copyright 2020 Xudong Zhang
- Language
- English
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (pages 118-124).
- Public Abstract (ETD)
The statistical depth has become increasingly popular as a powerful tool to analyze complex data over the past decades. It ranks the data from center-outward based on the data “centrality” or “outlyingness”. The statistical depth has been applied in a lot of tasks, such as data visualization, outlier detection, robust estimation, etc. The functional data are data of curves or trajectories that are widely used in a wealth of applications, such as environmentology, healthcare, economics, and biology. In this thesis, we study new functional data depths and explore their applications. Comparing to existing functional data depth, the proposed functional depths are strongly sensitive to a wide range of curve shape outlyingness while maintaining the capability of detecting functional location outliers. We study both the theoretical properties of the proposed functional data depths and their performances in applications such as outlier detection and robust estimation using simulated and real-world data sets.
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9983949492002771