Date of Award
5-2017
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
Mathematical Sciences
Thesis Sponsor/Dissertation Chair/Project Chair
Andrada E. Ivanescu
Committee Member
Haiyan Su
Committee Member
Andrew McDougall
Abstract
Several methods for performing the identification of outliers are described when dealing with functional data. The methods studied include prediction intervals for detection of dynamic functional outliers as well as related methods from the functional data literature. A comparison of methods is performed using metrics for dynamic outlier identification. Simulations and applications to environmental studies illustrate the applicability of the methods. Results obtained from simulation and application to real dataset suggest that Dynamic Function-on-Function Regression is a preferable method for detecting dynamic outliers. This method can detect outliers at a very high identification rate. Identification rate of dynamic outliers increases when a large number of curves and large size of outliers is observed.
File Format
Recommended Citation
Baruah, Kangkana Sarmah, "Identification of Dynamic Outliers" (2017). Theses, Dissertations and Culminating Projects. 352.
https://digitalcommons.montclair.edu/etd/352