Bayesian Analysis of Curves Shape Variation through Registration and Regression

This manuscript reviews the use of Bayesian hierarchical curve registration in Biostatistics and Bioinformatics.Several models allowing for unit-specific random time scales are discussed and applied to longitudinal dataarising in biomedicine, pharmacokinetics and time-course genomics. We consider representations of random functionals based on P-spline priors. Under this framework, straightforward posterior simulation strategies are outlined for inference.Beyond curve registration, we discuss jointregression modeling of both random effects and population level functional quantities. Finally, the use of mixture priors is discussed in the setting of differential expression analysis