Bootstrapping functional data: A study of distributional property of sample eigenvalues
Modern computer technology has facilitated the presence of high-dimensional data, whose graphical representations are curves, images or shapes. Because of the high-dimensionality, a dimension reduction such as functional principal component analysis or singular value decomposition is often employed. By using functional principal component analysis, a set of observed high-dimensional data can be decomposed into functional principal components and their uncorrelated principal component scores,...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of MODSIM 2011 International Congress on Modelling and Simulation|
|Access Rights:||Open Access|
|01_Shang_Bootstrapping_functional_data%3A_2011.pdf||351.5 kB||Adobe PDF|
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