Theoretical foundations of functional data analysis, with an introduction to linear operators

By T. Hsing, Randall Eubank. 2015

125 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/913b8c61e11606f4d956a7c721e1684caed95286

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In Control and Systems Engineering

1. Frank No'e
2. Hao Wu
3. Zhenhua Lin
4. Markus Reiß
5. Fang Yao
6. Martin Wahl

Citations in the corpus

• Intrinsic Riemannian functional data analysis (2018, methodology): “ector-valued random process (called log-process in Section2.2) that can be regarded as a random element in a tensor Hilbert space. Second, the rigorous theory of functional data analysis formulated inHsing and Eubank (2015) by random elements in separable Hilbert spaces fully applies to the log-proc”
• Variational Approach for Learning Markov Processes from Time Series Data (2017, methodology): “7 in [21]).”
• Nonasymptotic upper bounds for the reconstruction error of PCA (2016, methodology): “uation (resp. the second Neumann series), see e.g. Kato [18]. The dierence of Lemmas 3.1 and 3.2 to the formulation in e.g. [19, Lemma 2] or [14, Theorem 5.1.4] is the form of the remainder term. In [19, 14] the remainder term is given by an integral over the resolvent, while the above results lead ”