All of the model fits in the JMP Pro Functional Data Explorer platform rely on basis function expansion, a method for capturing non-linear relationships using a set of independent functions. See how basis function models work for modeling functional data. The video covers:
- B-Splines - Fits a basis spline (B-Spline) model to the data. Use the B-Spline model for non-periodic data.
- P-Splines - Fits a penalized basis spline (P-Spline) model to the data.
- Fourier Basis - Fits a Fourier Basis model to the data. Use the Fourier Basis model for periodic data. A periodic model assumes that the function finishes where it starts. See Fourier Basis Model.
- Wavelets - Fits several wavelets models to the data. A Wavelets model is a type of basis function model that is useful for data that contain a lot of peaks. This option requires data to be on an evenly spaced grid. If data is not evenly spaced, a grid is automatically created before the wavelet routine.
Learn more about Basis Function Expansion models and see them demonstrated in JMP Pro.
For more information on Wavelets models, consider viewing A Deeper Dive into Wavelets Models for Functional Data Analysis.