Theory-constrained Data-driven Model Selection, Specification, and Estimation: Applications in Discrete Choice Models
This thesis provides a framework, along with demonstrated applications, for carefully bringing data-driven flexibility to the specification and model selection of discrete choice models; while, at the same time, maintaining usability for analysis. Assumptions brought to bear under the classical theo...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143299 https://orcid.org/ 0000-0002-0829-1696 |