Estimation of Impact Ranges for Functional Valued Predictors

Music City Center 

Spectroscopy is essential for scientific and industrial applications, enabling the analysis of complex materials and their interactions with radiation. Hyperspectral remote sensing, or imaging spectroscopy, plays a key role in Earth sciences, including ecology, geology, and cryosphere research. With the growing availability of orbital imaging spectrometers, developing methods to enhance data utility is crucial. Identifying diagnostic absorption features in spectra is vital for understanding spectral-response relationships. This study considers a Functional Partial Least Squares (FPLS) approach to model spectral data as smooth functions and analyze their impact within specific impact ranges. We propose a two-stage estimation procedure to determine these ranges' midpoints and half-lengths, along with an iterative algorithm to estimate their number and locations. The method is validated through simulations and applied to real spectral data to identify diagnostic absorption features for predicting soil calcium carbonate (CaCO₃) content, successfully estimating their number and locations.

Functional Data Analysis

Functional Partial Least Squares

Spectroscopy

Impact Range 

Section on Statistics and the Environment