Forecasting short-term mortality trends using Bernstein polynomials

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.

Keywords:

• Forecasting
• mortality
• Bernstein polynomials
• simulated annealing

AMS CLASSIFICATION:

• 62P25
• 65C05

Acknowledgments

The authors thank the Human Mortality Database provider for their generosity of sharing the data with us. The Human Mortality Database is available at www.mortality.org. The authors thank the editor and the referee for their valuable comments that significantly improve the quality of the paper.

Additional information

Funding

The research is partially supported by the Ministry of Science and Technology of Taiwan under Grants MOST 108-2118-M-033-001 (Yuh-Jenn Wu), and MOST 106-2118-M-037-001 and MOST 108-2118-M-037-001-MY2 (Li-Chu Chien).