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Maximal autocorrelation factors for function-valued spatial/temporal data

Hooker, Giles; Roberts, Steven; Shang, Hanlin


Dimension reduction techniques play a key role in analysing functional data that possess temporal or spatial dependence. Of these dimension reduction techniques functional principal components analysis (FPCA) remains a popular approach. Functional principal components extract a set of latent components by maximizing variance in a set of dependent functional data. However, this technique may fail to adequately capture temporal or spatial autocorrelation. Functional maximum autocorrelation...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand
DOI: .36334/modsim.2015.a3.hooker
Access Rights: Open Access


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