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Functional principal component analysis is the preliminary step to represent the data in a lower dimensional space and to capture the main modes of variability of the data by means of small number of components which are linear combinations of original variables. Sensitivity of the variance and the covariance functions to irregular observations make this method vulnerable to outliers and may not capture...
We study the finite sample performance of predictors in the functional (Hilbertian) autoregressive model $${X_{n+1} = \Psi(X_n)+\varepsilon_n}$$ . Our extensive empirical study based on simulated and real data reveals that predictors of the form $${\hat\Psi(X_n)}$$ are practically optimal in a sense that their prediction errors are comparable with those of the infeasible perfect predictor...
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