Learning structure in nested logit models
Name
1129597025-MIT.pdf
Size
755.85 KB
Format
Adobe PDF
Checksum (MD5)
6698a1280991de941393d9d48040afe4
Author(s)
Aboutaleb, Youssef Medhat.
Advisor(s)
Moshe Ben-Akiva and Patrick Jaillet.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
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).
Subjects
Civil and Environmental Engineering.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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