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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{2:30 p.m., Tuesday, February 22, 2000}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 2:15 p.m. in 1st Floor Lounge)}

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\centerline{\sl Tianwen Cai}
\centerline{\sl Purdue University}
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\centerline{\bf Analysis of Spectral Data via  Wavelets and Functional Linear Model}
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Spectroscopy is widely used as a tool for chemical identification and
concentration calibration. As data collection and detection techniques
improve, a fundamental remaining limitation to the speed
and reliability with which chemical identification and imaging may be
performed is the degree to which spurious interfering signals, including
random  noise and broadband background emission, can be
effectively separated from spectral features of interest. In this talk we
present a wavelet procedure for suppressing  random noise and broadband
background emission and enhancing the useful chemical information content
of Raman spectra and spectral images. We also consider the problem of
concentration calibration using spectral data. A functional linear model
together with a variable selection technique is introduced for predicting
chemical concentrations based on observed spectra.

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