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Bayesian function-on-function regression for multilevel functional data.


ABSTRACT: Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images.

SUBMITTER: Meyer MJ 

PROVIDER: S-EPMC4575250 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Bayesian function-on-function regression for multilevel functional data.

Meyer Mark J MJ   Coull Brent A BA   Versace Francesco F   Cinciripini Paul P   Morris Jeffrey S JS  

Biometrics 20150318 3


Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regress  ...[more]

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