A Bayesian method for simultaneous registration and clustering of functional observations

Zizhen Wu and David B. Hitchcock

Computational Statistics & Data Analysis, 2016, vol. 101, issue C, 121-136

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.

Keywords: Functional data; Time warping; Curve registration (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:101:y:2016:i:c:p:121-136

DOI: 10.1016/j.csda.2016.02.010

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