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153860 https://hdl.handle.net/2286/R.I.34801 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2015 iv, 27 pages : illustrations (some color) Masters Thesis Academic theses Text eng Van Schaijik, Maria Kamarianakis, Yiannis Reiser, Mark R. Stufken, John Arizona State University Partial requirement for: M.S., Arizona State University, 2015 Includes bibliographical references (page 27) Field of study: Industrial engineering 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 perform variable selection, thus potentially reducing some of the variance in estimating the threshold parameter. This paper discusses the results of a study in which two different underlying model structures were simulated. The first is a regression model with correlated predictors, whereas the second is a self-exciting threshold autoregressive model. Finally the proposed Lasso-type methods are compared to conventional methods in an application to urban traffic data. Statistics Lasso SETAR Threshold Regression Threshold logic City traffic--Mathematical models. City traffic Threshold regression estimation via lasso, elastic-net, and lad-lasso: a simulation study with applications to urban traffic data