localizationProcesses

<p>matrix p by n, being n the number of functions and p the number of grid points.</p>

<p>Compute the localization processes of order k of the curve <code>y0</code>.</p>

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data
to solve problems of estimating unobserved segments of a partially observed functional data sample,
functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample.
Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria.
The package allows the implementation of the methodology and the replication of the results in El<c3><ad>as, A., Jim<c3><a9>nez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

Antonio El<c3><ad>as

localFDA

localizationProcesses: Localization processes

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