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A Bayesian method for simultaneous registration and clustering of functional observations

Author:
Wu, Zizhen, Hitchcock, David B.
Source:
Computational statistics & data analysis 2016 v.101 pp. 121-136
ISSN:
0167-9473
Subject:
Bayesian theory, algorithms, cell cycle, genes, growth curves, yeasts
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.
Agid:
6075533