dc.date.accessioned | 2020-06-03T09:41:24Z | |
dc.date.available | 2020-06-03T09:41:24Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | http://hdl.handle.net/10852/76579 | |
dc.description.abstract | In the last decades the analytical value of data has really become apparent and the amount of data collected has vastly increased. This enables us to approach problems in more data driven manners. In the thesis, I have combined recent developments in machine learning with statistical methods to better answer the question: “When in the future will a given event occur?”
The first part of the thesis was done in collaboration with the Norwegian bank DNB. We created new methods for predicting when in the future customers will default on their mortgage loans. By investigating the historical balances of the customers’ checking accounts, savings accounts and credit cards, we found that we could improve on existing methods for predicting mortgage defaults.
In the second part of the thesis, our attention was directed toward more general methodology that may be applied to a number of problems. Our proposed improvements were illustrated using a selection of available datasets, ranging from how gene and protein expression profiles affect the mortality of breast cancer patients, to how customer information can help determine if customers are likely to continue to subscribe to a music streaming service. | en_US |
dc.language.iso | en | en_US |
dc.relation.haspart | Paper I: Håvard Kvamme, Nikolai Sellereite, Kjersti Aas, and Steffen Sjursen. Predicting Mortgage Default Using Convolutional Neural Networks. Expert Systems with Applications, 102: 207–217, 2018. doi: 10.1016/j.eswa.2018.02.029. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.eswa.2018.02.029 | |
dc.relation.haspart | Paper II: Håvard Kvamme, Ørnulf Borgan, and Ida Scheel. Time-to-Event Prediction with Neural Networks and Cox Regression. Journal of Machine Learning Research, 20(129): 1–30, 2019. The article is included in the thesis. Also available in DUO: http://urn.nb.no/URN:NBN:no-79162 | |
dc.relation.haspart | Paper III: Håvard Kvamme and Ørnulf Borgan. Continuous and Discrete-Time Survival Prediction with Neural Networks. Submitted for publication. To be published. The paper is not available in DUO awaiting publishing. | |
dc.relation.haspart | Paper IV: Håvard Kvamme and Ørnulf Borgan. The Brier Score under Administrative Censoring: Problems and Solutions. Submitted for publication. To be published. The paper is not available in DUO awaiting publishing. | |
dc.relation.uri | https://doi.org/10.1016/j.eswa.2018.02.029 | |
dc.relation.uri | http://urn.nb.no/URN:NBN:no-79162 | |
dc.title | Time-to-Event Prediction with Neural Networks | en_US |
dc.type | Doctoral thesis | en_US |
dc.creator.author | Kvamme, Håvard | |
dc.identifier.urn | URN:NBN:no-79683 | |
dc.type.document | Doktoravhandling | en_US |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/76579/4/PhD-Kvamme-2020.pdf | |