  This study introduces a novel Generalized Additive Mixed Model (GAMM) for
mortality modelling, employing mortality covariates $k_t$ and $k_{ct}$ as
proposed by Dastranj- Kol\'a\vr (DK-LME). The GAMM effectively predicts
age-specific death rates (ASDRs) in both single and multi-population contexts.
Empirical evaluations using data from the Human Mortality Database (HMD)
demonstrate the model's exceptional performance in accurately capturing
observed mortality rates. In the DK-LME model, the relationship between log
ASDRs, and $k_t$ did not provide a perfect fit. Our study shows that the GAMM
addresses this limitation. Additionally, as discussed in the DK-LME model,
ASDRs represent longitudinal data. The GAMM offers a suitable alternative to
the DK-LME model for modelling and forecasting mortality rates. We will compare
the forecast accuracy of the GAMM with both the DK-LME and Li-Lee models in
multi-population scenarios, as well as with LC models in single population
scenarios. Comparative analyses highlight the GAMM's superior sample fitting
and out-of-sample forecasting performance, positioning it as a promising tool
for mortality modelling and forecasting.
