This paper is devoted to nonparametric analysis of functional data. We give asymptotic results for a kNN generalized regression estimator when the observed variables take values in any abstract space. The main novelty is our uniform consistency result (with rates).

We develop a doubly spectral representation of a stationary functional time series, and study the properties of its empirical version. The representation decomposes the time series into an integral of uncorrelated frequency components (Cramér representation), each of which is in turn expanded...

Data in which each observation is a curve occur in many applied problems. This paper explores prediction in time series in which the data is generated by a curve-valued autoregression process. It develops a novel technique, the predictive factor decomposition, for estimation of the...