A unified framework for multiple-exposure DLNMs is developed with estimation, AIC selection, and stacking, applied to Ontario air pollution and respiratory mortality data where stacking detects significant mixture effects.
Plugging this into Equation S.8, we have α= [ 1,α∗⊤ ]⊤ / ([ 1,α∗⊤ ]⊤[ 1,α∗⊤ ])1/2 which is the reparameterization used in literature
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A Unified Framework for Multiple Exposure Distributed Lag Non-Linear Models for Air Pollution Epidemiology
A unified framework for multiple-exposure DLNMs is developed with estimation, AIC selection, and stacking, applied to Ontario air pollution and respiratory mortality data where stacking detects significant mixture effects.