Mathematical Sciences: Multilinear Analysis of SpectroscopicData

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This collaborative project between a statistician and a biophysical chemist will refine and extend analyses of spectroscopic data. Spectroscopy is the measurement of the absorption or emission of particles from a specimen. The fluorescence spectra of proteins are highly sensitive to many different elements, and are frequently severely overlapping. The novel feature of the spectroscopic data is its high signal-to- noise ratio. The new statistical techniques will make use of the special features of the data and address the analysis to specific questions of interest. In standard factor analysis, the expectation of the response variable is assumed to be a linear function of unknown parameters. The nature of the spectroscopic data make it necessary to use non-linear functions of the parameters.