Lee-Carter model and Kernel PCA
This thesis investigates the application of Kernel Principal Component Analysis (KPCA) method on the Lee-Carter model, which is a two-step model for estimating and forecasting mortality rates (Lee and Carter, 1992). The motivation comes from the possible non-linearity of mortality data which cannot...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156935 |
| _version_ | 1826111339189239808 |
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| author | Wu, Yuanqi |
| author2 | Pan Guangming |
| author_facet | Pan Guangming Wu, Yuanqi |
| author_sort | Wu, Yuanqi |
| collection | NTU |
| description | This thesis investigates the application of Kernel Principal Component Analysis (KPCA) method on the Lee-Carter model, which is a two-step model for estimating and forecasting mortality rates (Lee and Carter, 1992). The motivation comes from the possible non-linearity of mortality data which cannot be captured by the traditional SVD and MLE methods. The proposed KPCA Lee-Carter model maps the mortality data into the feature space using kernel functions. Experiments on various kernels are conducted. The kernel and its corresponding parameters with the lowest forecasting error in k-fold cross validation are selected. The empirical analysis is conducted on U.S. mortality data to evaluate the model performance and simulation study is conducted to prove model correctness. |
| first_indexed | 2024-10-01T02:49:04Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156935 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:49:04Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1569352023-02-28T23:11:35Z Lee-Carter model and Kernel PCA Wu, Yuanqi Pan Guangming Zhu Wenjun School of Physical and Mathematical Sciences GMPAN@ntu.edu.sg, wjzhu@ntu.edu.sg Science::Mathematics::Statistics Business::Finance::Insurance::Mathematical models This thesis investigates the application of Kernel Principal Component Analysis (KPCA) method on the Lee-Carter model, which is a two-step model for estimating and forecasting mortality rates (Lee and Carter, 1992). The motivation comes from the possible non-linearity of mortality data which cannot be captured by the traditional SVD and MLE methods. The proposed KPCA Lee-Carter model maps the mortality data into the feature space using kernel functions. Experiments on various kernels are conducted. The kernel and its corresponding parameters with the lowest forecasting error in k-fold cross validation are selected. The empirical analysis is conducted on U.S. mortality data to evaluate the model performance and simulation study is conducted to prove model correctness. Bachelor of Science in Mathematical Sciences 2022-04-29T03:30:27Z 2022-04-29T03:30:27Z 2022 Final Year Project (FYP) Wu, Y. (2022). Lee-Carter model and Kernel PCA. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156935 https://hdl.handle.net/10356/156935 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics::Statistics Business::Finance::Insurance::Mathematical models Wu, Yuanqi Lee-Carter model and Kernel PCA |
| title | Lee-Carter model and Kernel PCA |
| title_full | Lee-Carter model and Kernel PCA |
| title_fullStr | Lee-Carter model and Kernel PCA |
| title_full_unstemmed | Lee-Carter model and Kernel PCA |
| title_short | Lee-Carter model and Kernel PCA |
| title_sort | lee carter model and kernel pca |
| topic | Science::Mathematics::Statistics Business::Finance::Insurance::Mathematical models |
| url | https://hdl.handle.net/10356/156935 |
| work_keys_str_mv | AT wuyuanqi leecartermodelandkernelpca |