Classifications of Proteomic Mass Spectra and Other Curve Data Xiaohui Wang, Ph.D Department of Mathematics University of Texas-Pan American Abstract
Disease studies based on
proteomic mass spectra seem promising. Classification of proteomic mass
spectra is a challenging task because of the features of the spectra.
Motivated from this problem, we propose classification models for
binary and multicategory data where the predictor is a random function.
Our methodology is Bayesian, using wavelet basis functions which have
nice approximation properties over a large class of functional spaces
and can accommodate a variety of functional forms observed in real life
applications.
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