WAVELET-BASED FEATURE EXTRACTION FOR MORTALITY PROJECTION

被引：9

作者：

Hainaut, Donatien ^{[1
]}

Denuit, Michel ^{[1
]}

机构：

[1] UCLouvain, Louvain Inst Data Anal & Modeling LIDAM, Inst Stat Biostat & Actuarial Sci ISBA, B-1348 Louvain La Neuve, Belgium

来源：

ASTIN BULLETIN | 2020年 / 50卷 / 03期

关键词：

Discrete wavelet transform; mortality smoothing; Poisson regression; regularization; Lasso; STOCHASTIC MORTALITY; EXTENSION; SHRINKAGE; MODEL;

D O I：

10.1017/asb.2020.18

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee-Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

引用

页码：675 / 707

页数：33

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