Measured bioresponses are often characterized by an intrinsic high frequency and strong persistent correlations inhibiting statistical modeling by traditional techniques. The talk overviews two novel wavelet-based techniques for modeling such challenging data.
Wavelet domains provide natural modeling environments for data that scale as well as for data consisting of continuous n-dimensional functions. We briefly discuss technicalities and describe in detail two applications. First application deals with wavelet analysis of functional ANOVA (FANOVA) where the observations are functional measurements coming from clinical research. The second application discusses use of wavelet-based measures of irregular scaling (multifractal spectra) in classification of high frequency pupillary responses for people with various eye pathologies.
