Bayesian function-on-function regression for multilevel functional data

Biometrics
Mark J MeyerJeffrey S Morris

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.

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References

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Citations

Nov 14, 2017·Statistical Modelling·Jeffrey S Morris, Veerabhadran Baladandayuthapani
May 12, 2018·Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences·Hongxiao ZhuRolf Müller
Feb 26, 2019·Journal of the American Statistical Association·Xiaoxiao SunPing Ma
Sep 29, 2020·Journal of the American Statistical Association·Hojin YangJeffrey S Morris
Jun 27, 2019·Journal of the American Statistical Association·Wonyul LeeJeffrey S Morris
Jun 17, 2021·Statistics in Medicine·Daniel R KowalMarie Lynn Miranda

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