Learning structure in nested logit models
Author(s)
Aboutaleb, Youssef Medhat.
Download1129597025-MIT.pdf (755.8Kb)
Other Contributors
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Moshe Ben-Akiva and Patrick Jaillet.
Terms of use
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Show full item recordAbstract
This work is about developing an estimation procedure for nested logit models that optimizes over the nesting structure in addition to the model parameters. Current estimation practices require an a priori specification of a nesting structure. We formulate the problem of learning an optimal nesting structure as a mixed integer nonlinear programming (MINLP) optimization problem and solve it using a variant of the linear outer approximation algorithm. We demonstrate that it is indeed possible to recover the nesting structure directly from the data by applying our method to synthetic and real datasets.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 67-68).
Date issued
2019Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Civil and Environmental Engineering., Electrical Engineering and Computer Science.