  This paper presents a novel approach for modeling mortality rates above age
70 by proposing a mixture-based model. This model is compared to four other
widely used models: the Beard, Gompertz, Makeham, and Perks models. Our model
can capture the complex behavior of mortality rates at all ages, providing a
more accurate representation of the data.
  To evaluate the performance of our model, we applied it to two countries with
different data quality: Japan and Brazil. Our results show that the proposed
model outperforms the other models in both countries, particularly in Japan
where it obtained an absolute mean percentage error of less than 7%, while the
other models presented values greater than 30%. This highlights the ability of
our model to adapt to different data quality and country-specific mortality
patterns.
  In summary, this paper presents a mixture-based model that captures the
behavior of mortality rates at all ages and outperforms other widely used
models in both high- and low-quality data settings. This model can improve
mortality prediction and inform public health policy.
