Like density functions, period life-table death counts are nonnegative and have a constrained integral, and thus live in a constrained nonlinear space. Implementing established modelling and forecasting methods without obeying these constraints can be problematic for such nonlinear data. We introduce cumulative distribution function transformation to forecast the life-table death counts. Using the Japanese life-table death counts obtained from the Japanese Mortality Database (2024), we evaluate the point and interval forecast accuracies of the proposed approach, which compares favourably to an existing compositional data analytic approach. The improved forecast accuracy of life-table death counts is of great interest to demographers for estimating age-specific survival probabilities and life expectancy and actuaries for determining temporary annuity prices for different ages and maturities.

翻译：与密度函数类似，周期生命表死亡计数具有非负性且积分受约束，因而存在于受约束的非线性空间中。对此类非线性数据，若采用既有的建模与预测方法而不遵循这些约束条件，可能会产生问题。本文引入累积分布函数变换方法来预测生命表死亡计数。基于日本死亡率数据库（2024）获取的日本生命表死亡计数数据，我们评估了所提方法的点预测与区间预测精度，其表现优于现有的成分数据分析方法。生命表死亡计数预测精度的提升对人口学家估算年龄别生存概率与预期寿命、以及精算师确定不同年龄与期限的临时年金价格具有重要价值。