Forecasting short-term mortality trends using Bernstein polynomials

Yuh-Jenn Wu, Li-Syuan Hong, Li-Hsueh Cheng and Li-Chu Chien

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 8, 2417-2433

Abstract: Mortality data play an important role on the fields such as health, epidemiology and national planning. Most mortality models mainly focus on providing a perfect fitting, to the detriment of an exact forecasting result. In this paper, we fit the Bernstein polynomial to mortality data based on maximum likelihood based inference through the simulated annealing method. The proposed method utilizes the derivative of Bernstein polynomials to describe the pattern of mortality rates. The asymptotic behavior of the proposed model is also given on general results. The performance of the proposed method is evaluated by simulated examples and illustrated through applications to datasets provided from the Human Mortality Database (www.mortality.org). The simulated examples and real data analysis show that the proposed approach is quite satisfactory in forecasting the short-term mortality trends.

Date: 2023
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DOI: 10.1080/03610926.2021.1952432

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