Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6868987 | Computational Statistics & Data Analysis | 2016 | 16 Pages |
Abstract
We develop a Bayesian method that simultaneously registers and clusters functional data of interest. Unlike other existing methods, which often assume a simple translation in the time domain, our method uses a discrete approximation generated from the family of Dirichlet distributions to allow warping functions of great flexibility. Under this Bayesian framework, a MCMC algorithm is proposed for posterior sampling. We demonstrate this method via simulation studies and applications to growth curve data and cell cycle regulated yeast genes.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Zizhen Wu, David B. Hitchcock,