DOI
10.17077/etd.siecbxeb
Document Type
Dissertation
Date of Degree
Summer 2018
Degree Name
PhD (Doctor of Philosophy)
Degree In
Statistics
First Advisor
Chan, Kung-Sik
First Committee Member
Chan, Kung-Sik
Second Committee Member
Ghosh, Joyee
Third Committee Member
Tan, Aixin
Fourth Committee Member
Breheny, Patrick
Fifth Committee Member
Lang, Joseph
Abstract
This dissertation research addresses how to detect structural changes in stochastic linear models. By introducing a special structure to the design matrix, we convert the structural change detection problem to a variable selection problem. There are many existing variable selection strategies, however, they do not fully cope with structural change detection. We design two penalized regression algorithms specifically for the structural change detection purpose. We also propose two methods involving these two algorithms to accomplish a bi-level structural change detection: they locate the change points and also recognize which predictors contribute to the variation of the model structure. Extensive simulation studies are shown to demonstrate the effectiveness of the proposed methods in a variety of settings. Furthermore, we establish asymptotic theoretical properties to justify the bi-level detection consistency for one of the proposed methods. In addition, we write an R package with computationally efficient algorithms for detecting structural changes. Comparing to traditional methods, the proposed algorithms showcase enhanced detection power and more estimation precision, with added capacity of specifying the model structures at all regimes.
Keywords
Changed variables, Change points, MDL, model, Structural change
Pages
x, 144 pages
Bibliography
Includes bibliographical references (pages 142-144).
Copyright
Copyright © 2018 Bo Wang
Recommended Citation
Wang, Bo. "Structural change detection via penalized regression." PhD (Doctor of Philosophy) thesis, University of Iowa, 2018.
https://doi.org/10.17077/etd.siecbxeb