Applications of operations research in finance and healthcare
Abstract/Contents
- Abstract
- This thesis covers several applications of Operations Research in the domains of finance and healthcare. There are three chapters, each covering a different application. Chapter 1 applies techniques from deep learning to estimate mortgage risk. The near-elimination of feature engineering is substituted by models with several thousand parameters that require large amounts of training data. The predictive performance of these models strongly exceeds that of baseline models, especially for predicting prepayments. This increased accuracy, however, comes at the cost of a more opaque model which is harder for a human to interpret than simpler models like logistic regression, which are currently the industry standard. The work in Chapter 2 explores the design of policies for biometric authentication. It first develops a model for the joint distribution of similarity scores associated with different fingers and irises. In the second step, this model is harnessed to design near-optimal multi-stage policies that would be used for authentication, and are robust to gaming, can be computed in real-time and are personalized for optimal performance. The work shows that a reduction of several orders of magnitude in the error rates is achievable by solely changing the authentication policies -- and leaving the hardware unchanged. Chapter 3 is motivated by a humanitarian cause geared towards helping developing countries -- to reduce mortality in children by identifying effective interventions at the planning stage. It takes a descriptive health model (called LiST), which estimates mortality of children given coverage of interventions, and embeds that into an optimization engine in order to minimize mortality under a fixed budget. In doing so, it allows LiST to be used in a prescriptive framework, where policymakers can identify the optimal intervention set at a fixed budget as well as recognize the trade-off of mortality reduction and budget allocation. We find that a greedy strategy offers near-optimal performance with ease of implementation. The findings also highlight the critical role that optimization plays in mortality reduction.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sadhwani, Apaar |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Giesecke, Kay |
| Primary advisor | Wein, Lawrence |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Wein, Lawrence |
| Thesis advisor | Ye, Yinyu |
| Advisor | Ye, Yinyu |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Apaar Sadhwani. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Apaar Sadhwani
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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