Gaussian-process deflators estimated from sparse pension-fund death counts give smoother and better-scoring mortality projections than constant or age-by-age fixed deflators.
Bayesian Dynamic Estimation of Mortality Schedules in Small Areas
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abstract
The determination of the shapes of mortality curves, the estimation and projection of mortality patterns over time, and the investigation of differences in mortality patterns across different small underdeveloped populations have received special attention in recent years. The challenges involved in this type of problems are the common sparsity and the unstable behavior of observed death counts in small areas (populations). These features impose many dificulties in the estimation of reasonable mortality schedules. In this chapter, we present a discussion about this problem and we introduce the use of relational Bayesian dynamic models for estimating and smoothing mortality schedules by age and sex. Preliminary results are presented, including a comparison with a methodology recently proposed in the literature. The analyzes are based on simulated data as well as mortality data observed in some Brazilian municipalities.
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Analyzing Pension Fund Mortality with Gaussian Processes in a Sub Population Framework
Gaussian-process deflators estimated from sparse pension-fund death counts give smoother and better-scoring mortality projections than constant or age-by-age fixed deflators.