A Functional Generalized Linear Model with application to Cervical Pre-cancer Diagnosis using Fluorescence spectroscopy
A functional generalized linear model is applied to spectroscopic data to discriminate disease from non-disease in the diagnosis of cervical pre-cancer. For each observation, multiple functional predictors are available, and it is of interest to select a few of them for efficient classification. In addition to multiple functional predictors, some non-functional covariates are also used to account for systematic spectra differences caused by these covariates. Ridge/Lasso-type penalties are included in the model to select among multiple curves. A truncated eigenfunction expansion is used to transform the functional predictors to finite dimensional space.
This is joint work with Dennis D. Cox