Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. We compare various robust smoothing methods for estimating fluorescence emission spectra and data driven methods for the selection of smoothing parameter. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory and we present a computationally efficient procedure that approximates robust leave-one-out cross validation.