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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 covariates are available, and it is of interest to select a few of them for efficient classification. In addition to multiple functional covariates, some non-functional covariates are also used to account for systematic differences caused by these covariates. Functional principal components are used to reduce the model to multivariate logistic regression and a grouped Lasso penalty is applied to the reduced model to select useful functional covariates among multiple curves.

A Functional Generalized Linear Model with Curve Selection in Cervical Pre-cancer Diagnosis Using Fluorescence Spectroscopy