Fast Computation and Bandwidth Selection Algorithms for Smoo (original) (raw)

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This paper examines data-driven estimation of the mean surface in nonparamet- ric regression for huge functional time series. In this framework, we consider the use of the double conditional smoothing (DCS), an equivalent but much faster translation of the 2D-kernel regression. An even faster, but again equivalent func- tional DCS (FCDS) scheme and a boundary correction method for the DCS/FCDS is proposed. The asymptotically optimal bandwidths are obtained and selected by an IPI (iterative plug-in) algorithm. We show that the IPI algorithm works well in practice in a simulation study and apply the proposals to estimate the spot-volatility and trading volume surface in high-frequency nancial data under a functional representation. Our proposals also apply to large lattice spatial or spatial-temporal data from any research area.

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Handle: RePEc:pdn:ciepap:143

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