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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丘政民 | zh_TW |
| dc.contributor.advisor | JENG-MIN CHIOU | en |
| dc.contributor.author | 鄭君淑 | zh_TW |
| dc.contributor.author | JYUN-SHU JHENG | en |
| dc.date.accessioned | 2024-08-07T17:14:25Z | - |
| dc.date.available | 2024-08-08 | - |
| dc.date.copyright | 2024-08-07 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-01 | - |
| dc.identifier.citation | Basellini, U., Camarda, C. G., and Booth, H. (2023). Thirty years on: A review of the lee–carter method for forecasting mortality. International Journal of Forecasting, 39(3):1033–1049.
Booth, H., Maindonald, J., and Smith, L. (2002). Applying lee-carter under conditions of variable mortality decline. Population studies, 56(3):325–336. Booth, H. and Tickle, L. (2008). Mortality modelling and forecasting: A review of methods. Annals of actuarial science, 3(12):3–43 Fan, J. and Gijbels, I. (1996). Local polynomial modelling and its applications. Number 66 in Monographs on statistics and applied probability series. Chapman & Hall, London [u.a.] Hilton, J., Dodd, E., Forster, J. J., and Smith, P. W. (2019). Projecting uk mortality by using bayesian generalized additive models. Journal of the Royal Statistical Society Series C: Applied Statistics, 68(1):29–49 Huang, J. Z., Shen, H., and Buja, A. (2009). The analysis of two way functional data using twoway regularized singular value decompositions. Journal of the American Statistical Association, 104(488):1609–1620. Hyndman, R. J. and Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10):4942–4956. Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting us mortality. Journal of the American statistical association, 87(419):659–671. Levantesi, S. and Pizzorusso, V. (2019). Application of machine learning to mortality modeling and forecasting. Risks, 7(1):26. Li, N. and Lee, R. (2005). Coherent mortality forecasts for a group of populations: An extension of the lee-carter method. Demography, 42:575–594. Liu, X. and Yu, H. (2011). Assessing and extending the lee-carter model for long-term mortality prediction. In Living to 100 Symposium. Nigri, A., Levantesi, S., Marino, M., Scognamiglio, S., and Perla, F. (2019). A deep learning integrated lee–carter model. Risks, 7(1):33. Pedroza, C. (2006). A bayesian forecasting model: predicting us male mortality. Biostatistics, 7(4):530–550. Reinsch, C. H. (1967). Smoothing by spline functions. Numerische mathematik, 10(3):177–183 Renshaw, A. E. and Haberman, S. (2006). A cohort based extension to the lee–carter model for mortality reduction factors. Insurance: Mathematics and economics, 38(3):556–570 Shang, H. L., Booth, H., and Hyndman, R. J. (2011). Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods. Demographic Research, 25:173–214. Shang, H. L., Smith, P. W., Bijak, J., and Wiśniowski, A. (2016). A multilevel functional data method for forecasting population, with an application to the united kingdom. International Journal of Forecasting, 32(3):629–649. Tuljapurkar, S., Li, N., and Boe, C. (2000). A universal pattern of mortality decline in the g7 countries. Nature, 405(6788):789–792. Wilmoth, J. R. (1993). Computational methods for fitting and extrapolating the lee-carter model of mortality change. Yang, W., Müller, H.G., and Stadtmüller, U. (2011). Functional singular component analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(3):303–324 Yao, F., Müller, H.G., and Wang, J.L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American statistical association, 100(470):577–590. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93770 | - |
| dc.description.abstract | 隨著人口結構的變化和社會老齡化的加劇，死亡率的建模和預測越來越受到關注，特別是在保險領域，準確預測死亡率有助於保險公司有效制定保單。在這項研究中，我們從Lee-Carter 模型出發，將原始的奇異值分解拓展到函數型奇異值分解，進而得到與時間和年齡有關的一對奇異函數，接著我們根據年份相關的奇異函數進行局部線性外推做預測，並將其預測表現與Lee-Carter模型進行比較。在實際數據分析中，我們使用了台灣的死亡率數據，結果我們的預測結果比Lee-Carter模型更準確，在模擬實驗亦得到相同的結論。 | zh_TW |
| dc.description.abstract | With demographic changes and society aging, the modeling and prediction of mortality rates have garnered increasing attention, especially in the insurance sector. Accurate mortality forecasts assist insurance companies in formulating policies effectively. In this study, we begin with the Lee-Carter model and extend the original singular value decomposition to a functional version, yielding a pair of time-specific and age-specific singular functions. Subsequently, we conduct local linear extrapolation based on these year-specific singular functions for forecasting, comparing their performance with that of the Lee-Carter model. In our real data analysis, we use mortality data from Taiwan to demonstrate that our forecasting performance is more accurate than that of the Lee-Carter model. This conclusion is further supported by our simulation study. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-07T17:14:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-07T17:14:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 i
Abstract iii Contents v List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Related methodology 5 2.1 Lee carter model 5 2.2 Hyndman–Ullah model 7 Chapter 3 Method 9 3.1 Functional singular component analysis 9 3.1.1 Estimation 11 3.1.1.1 Estimation of mean function 11 3.1.1.2 Estimation of cross covariance function 11 3.1.2 Estimation of singular elements 12 3.2 Regularized singular value decomposition 12 3.3 Local Linear Extrapolation 14 Chapter 4 Mortality data application 17 4.1 Historical data 17 4.2 Model fitting 18 4.3 Forecasting 21 4.4 Forecasting accuracy 24 Chapter 5 Simulation study 29 5.1 Simulation setting 29 5.2 Simulation result 31 Chapter 6 Conclusion and discussion 37 6.1 Conclusion 37 6.2 Future work 37 References 39 Appendix A — Table of MAE between rank-one and rank-two using FSVD, RSVD 43 A.1 Female 43 A.2 Male 44 | - |
| dc.language.iso | en | - |
| dc.subject | 函數型資料 | zh_TW |
| dc.subject | 死亡率 | zh_TW |
| dc.subject | 奇異值分解 | zh_TW |
| dc.subject | 局部線性外插 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | forecast | en |
| dc.subject | functional data analysis | en |
| dc.subject | age-specific mortality | en |
| dc.subject | singular value decomposition | en |
| dc.subject | local linear extrapolation | en |
| dc.title | 預測年齡死亡率之比較研究 | zh_TW |
| dc.title | A comparative study of age-specific mortality forecast | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李百靈;蔡碧紋 | zh_TW |
| dc.contributor.oralexamcommittee | Pai-Ling Li;Pi-Wen Tsai | en |
| dc.subject.keyword | 函數型資料,死亡率,奇異值分解,局部線性外插,預測, | zh_TW |
| dc.subject.keyword | functional data analysis,age-specific mortality,singular value decomposition,local linear extrapolation,forecast, | en |
| dc.relation.page | 44 | - |
| dc.identifier.doi | 10.6342/NTU202402699 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-08-05 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 統計與數據科學研究所 | - |
| 顯示於系所單位： | 統計與數據科學研究所 | |
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