WorkshopsModeling repeatedly observed functional data
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Abstract: Repeatedly observed and thus dependent functional data are encountered when random curves are recorded repeatedly for each subject in a sample. The proposed models lead to an interpretable and straightforward decomposition of the inherent variation in repeatedly observed functional data and are implemented through a two-step functional principal component analysis. The time points where functions are recorded may be irregular and sparse as is often the case in longitudinal studies. The estimated model components are shown to be consistent in various scenarios. The methods are illustrated through the analysis of longitudinal mortality data from period life tables that are repeatedly observed for various countries over many years, and also through simulation studies. This talk is based on joint work with Kehui Chen.
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Hans-Georg Muller
2012-12-21
13:30:00 - 14:20:00
308 , Mathematics Research Center Building (ori. New Math. Bldg.)
Abstract: Repeatedly observed and thus dependent functional data are encountered when random curves are recorded repeatedly for each subject in a sample. The proposed models lead to an interpretable and straightforward decomposition of the inherent variation in repeatedly observed functional data and are implemented through a two-step functional principal component analysis. The time points where functions are recorded may be irregular and sparse as is often the case in longitudinal studies. The estimated model components are shown to be consistent in various scenarios. The methods are illustrated through the analysis of longitudinal mortality data from period life tables that are repeatedly observed for various countries over many years, and also through simulation studies. This talk is based on joint work with Kehui Chen.