Abstract
The notions of depth for functional data provide a way of ordering curves from center-outward. These methods are designed for trajectories that are observed on a fine grid of equally spaced time points. However, in many applications the trajectories are observed on sparse irregularly spaced time points. We propose a model-based consistent procedure for estimating the depths when the curves are observed on sparse and unevenly spaced points.
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© 2011 Springer-Verlag Berlin Heidelberg
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Lòpez-Pintado, S., Wei, Y. (2011). Depth for Sparse Functional Data. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_32
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DOI: https://doi.org/10.1007/978-3-7908-2736-1_32
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2735-4
Online ISBN: 978-3-7908-2736-1
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