American Statistical Association New York City Metropolitan Area Chapter Mailman School of Public Health Columbia University Department of Biostatistics Colloquium

We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The model can be viewed as the functional analog of the classical longitudinal mixed effects model where random effects are replaced by random processes. Methods have wide applicability and are computationally feasible for moderate and large data sets. Computational feasibility is assured by using principal component bases for the functional processes. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study designed to analyze differences and changes in brain connectivity in healthy volunteers and multiple sclerosis (MS) patients.

Dr. Crainiceanu is an Associate Professor in the Department of Biostatistics at Johns Hopkins University. He obtained his Ph.D. in Statistics from Cornell University in 2003 under the supervision of David Ruppert. His major areas of statistical expertise are functional data analysis, nonparametrics, Bayesian inference and measurement error modeling. He is the co-author of the popular book "Measurement Error in Nonlinear Models: A Modern Perspective". Dr. Crainiceanu has a wide array of scientific interests with most of his recent work in the area of neurosciences and sleep research. Together with Brian Caffo, he is the leader of the research group on "Statistical methods for new technologies" at Johns Hopkins University. Dr. Crainiceanu is the program chair for the ENAR 2011 in Miami and for the ``Statistical Methods for Very Large Data Sets" to be held between June 1-3, 2011 in Baltimore.