Abstract
We propose kernel methods for estimating covariance functions, when the data consists of a collection of curves. Every curve is modelled as an independent realization of a stochastic process with unknown mean and covariance structure. We consider a kernel density estimator, which has the positive semi-definiteness property on the “time” points and also in the continuum. We describe a cross-validation procedure, which leaves out an entire curve at a time, to choose the bandwidth (smoothing parameter) automatically from the observed collection of curves.
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Pallini, A. (1999). Kernel Methods For Estimating Covariance Functions From Curves. In: Vichi, M., Opitz, O. (eds) Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60126-2_40
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DOI: https://doi.org/10.1007/978-3-642-60126-2_40
Publisher Name: Springer, Berlin, Heidelberg
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