Properties of principal component methods for functional and longitudinal data analysis
The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analysis a random function typically represents a patient, or subject, who is observed at only a...[Show more]
|Collections||ANU Research Publications|
|Source:||Annals of Statistics 2006, Vol. 34, No. 3, 1493-1517|
|Hall Muller and Wang Poperties of principal component methods 2006.pdf||266.77 kB||Adobe PDF|
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