Maximal autocorrelation functions in functional data analysis

Giles Hooker; Steven Roberts

doi:10.1007/s11222-015-9582-5

Maximal autocorrelation functions in functional data analysis

Giles Hooker, Steven Roberts^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.

Original language	English
Pages (from-to)	945-950
Number of pages	6
Journal	Statistics and Computing
Volume	26
Issue number	5
DOIs	https://doi.org/10.1007/s11222-015-9582-5
Publication status	Published - 1 Sept 2016

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title = "Maximal autocorrelation functions in functional data analysis",

abstract = "This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.",

keywords = "Factor rotation, Functional data, Interpretability, Principal components analysis",

author = "Giles Hooker and Steven Roberts",

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AB - This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.

KW - Factor rotation

KW - Functional data

KW - Interpretability

KW - Principal components analysis

UR - https://www.scopus.com/pages/publications/84930883516

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DO - 10.1007/s11222-015-9582-5

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JO - Statistics and Computing

JF - Statistics and Computing

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Maximal autocorrelation functions in functional data analysis

Abstract

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