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Threshold Regression Estimation via Lasso, Elastic-Net, and Lad-Lasso: A Simulation Study with Applications to Urban Traffic Data

Abstract Threshold regression is used to model regime switching dynamics where the effects of the explanatory variables in predicting the response variable depend on whether a certain threshold has been crossed. When regime-switching dynamics are present, new estimation problems arise related to estimating the value of the threshold. Conventional methods utilize an iterative search procedure, seeking to minimize the sum of squares criterion. However, when unnecessary variables are included in the model or certain variables drop out of the model depending on the regime, this method may have high variability. This paper proposes Lasso-type methods as an alternative to ordinary least squares. By incorporating an L_{1} penalty term, Lasso methods perfor... (more)
Created Date 2015
Contributor van Schaijik, Maria (Author) / Kamarianakis, Yiannis (Advisor) / Kamarianakis, Yiannis (Committee member) / Reiser, Mark (Committee member) / Stufken, John (Committee member) / Arizona State University (Publisher)
Subject Statistics / Lasso / SETAR / Threshold Regression
Type Masters Thesis
Extent 32 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Industrial Engineering 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis