Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
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Scientists and statisticians often want to learn about the complex relationships that connect two variables that vary over time. Recent work on sparse functional historical linear models confirms that they are promising for this purpose, but several notable limitations exist. Most importantly, previous works have imposed sparsity on the coefficient function, but have not allowed the sparsity, hence lag, to vary with time. We simplify the framework of sparse functional historical linear models by using a rectangular coefficient structure along with Whittaker smoothing, then relax the previous frameworks by estimating the dynamic time lag from a hierarchical coefficient structure. We motivate our study by aiming to extract the physical rainfall-runoff processes hidden within hydrological data. We show the promise and accuracy of our method using four simulation studies, justified by two real sets of hydrological data.
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