Functional Mixed Registration Models

Functional Mixed Registration Models

Tuesday, November 3, 2009
  3:40–5 p.m.

Assistant Professor Donatello Telesca from the Department of Biostatistics, University of California, Los Angeles

will present

"Functional Mixed Registration Models"

Abstract:
Functional data often exhibit a common shape but also variations in amplitude and phase across curves. The analysis often proceeds by synchronization of the data through curve registration. We propose a Bayesian hierarchical model for curve registration. Our methodology is extended to define a class of probability models, which combine curve registration with functional mixed effects modeling, discriminating phase and amplitude variability in a joint fashion. We discuss this class of models with a focus on penalized smoothing splines and propose Bayesian inferential procedures based on Markov Chain Monte Carlo samples from the posterior distribution of the functions of interest. We illustrate the application of our model using simulated data as well as to two datasets, namely, the Berkeley study on human growth and a study on the pharmacokinetics of the drug Remifentanil. Time permitting; we will introduce a generalized view of curve registration with applications to longitudinal counts of criminal activity.

Education:
Ph.D.Statistics, University of Washington, Seattle
MS Statistics, University of Washington, Seattle
B.S. Economics and Statistics, Universit a Luigi Bocconi, Milano, Italy

Research Interests:
Dr. Telesca’s research mainly focuses on dependent data and nonparametrics. Other areas of interest are Decision Theory, MCMC computation and Statistical Methods in Bioinformatics. estimation, and computation in the social sciences.