Mortality forecasting is recast as integrating a flow field through the low-dimensional Tucker decomposition score space of the Human Mortality Database, yielding lower bias and error than Lee-Carter, Hyndman-Ullah, or UN models in cross-validation.
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The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.
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Mortality Forecasting as a Flow Field in Tucker Decomposition Space
Mortality forecasting is recast as integrating a flow field through the low-dimensional Tucker decomposition score space of the Human Mortality Database, yielding lower bias and error than Lee-Carter, Hyndman-Ullah, or UN models in cross-validation.
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A penalized distributed lag non-linear Lee-Carter framework for regional weekly mortality forecasting
The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.