I will share my way through figuring out this complicated, and, frankly, cutting-edge modeling and statistical framework for anyone who may need to understand the methodology and its application to time-series data analysis.
Distributed lag non-linear models (DLNMs) are a modelling framework used to describe, flexibly and simultaneously, linear or non-linear delayed effects between predictors and an outcome, a dependency defined as an exposure-lag-response association. These models are mainly used to assess the impact of environmental factors and climate change on health. I will focus on mathematical and statistical concepts that underpin this modelling framework, as well as the cross-basis that describes the bidimensional functions for exposure-response and lag-response spaces and represents the core of DLNMs.
Look out for a blogpost coming soon on this!
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