Maximal autocorrelation factors for function-valued spatial/temporal data
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]
|Collections||ANU Research Publications|
|Source:||MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand|
|Access Rights:||Open Access|
|01_Hooker_Maximal_autocorrelation_2015.pdf||4.91 MB||Adobe PDF|
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