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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, November 3, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl I-Shou Chang}
\centerline{\sl National Health Research Institute}
\centerline{\sl Taipei, Taiwan}
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\centerline{\bf Bayesian Isotonic Regression Using Bernstein Polynomials, 
with Application to Microarray Data}
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Abstract:

Bayesian isotonic regressions are studied using priors on Bernstein
polynomials and Markov chain Monte Carlo methods.  These priors are
flexible and have support the space of bounded, increasing, and continuous
functions satisfying certain geometric properties, such as being convex or
sigmoidal.  As an application, a Bayesian isotonic and sigmoidal
regression model is successfully employed to conduct data normalization in
cDNA microarray experiments with DNA control sequences, where calibration
curves relating fluorescence signal intensities to gene expressional
levels are studied as regression functions.

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