Mapping-to-Parameter Nonlinear Functional Regression with Novel B-spline Free Knot Placement Algorithm
This addresses complex functional regression problems in fields like statistics or data science, but appears incremental as it builds on existing B-spline methods with a new knot placement strategy.
The paper tackles nonlinear functional regression by proposing a Mapping-to-Parameter model that maps functions to a parameter space using B-splines with a novel free knot placement algorithm, demonstrating effectiveness in real data applications compared to state-of-the-art methods.
We propose a novel approach to nonlinear functional regression, called the Mapping-to-Parameter function model, which addresses complex and nonlinear functional regression problems in parameter space by employing any supervised learning technique. Central to this model is the mapping of function data from an infinite-dimensional function space to a finite-dimensional parameter space. This is accomplished by concurrently approximating multiple functions with a common set of B-spline basis functions by any chosen order, with their knot distribution determined by the Iterative Local Placement Algorithm, a newly proposed free knot placement algorithm. In contrast to the conventional equidistant knot placement strategy that uniformly distributes knot locations based on a predefined number of knots, our proposed algorithms determine knot location according to the local complexity of the input or output functions. The performance of our knot placement algorithms is shown to be robust in both single-function approximation and multiple-function approximation contexts. Furthermore, the effectiveness and advantage of the proposed prediction model in handling both function-on-scalar regression and function-on-function regression problems are demonstrated through several real data applications, in comparison with four groups of state-of-the-art methods.