This thesis explores extensions of Random Utility Models (RUMs), providing more flexible models and adopting a computational perspective. This includes building new models and understanding their properties such as identifiability and the log concavity of their likelihood functions as well as the development of estimation algorithms. / Engineering and Applied Sciences
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/12274551 |
| Date | 06 June 2014 |
| Creators | Azari Soufiani, Hossein |
| Contributors | Parkes, David C. |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Rights | open |
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