Local principal differential analysis: Graphical methods for functional data with covariates

We focus on principal differential analysis (PDA) of functional data for obtaining a low-dimensional representation of a collection of curves. PDA assumes there exists a linear differential operator that results in the zero-function when it is applied to each of the data curves, or equivalently, tha...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2017-03, Vol.46 (3), p.2346-2359
Main Authors: Staniswalis, Joan G., Dodoo, Christopher, Sharma, Anu
Format: Article
Language:English
Subjects:
CAEP curves
Collection
Computer simulation
Differential operator
Equivalence
Estimates
Exploration
Low-dimensional approximation
Operators
PDA
Representations
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Description
Summary:We focus on principal differential analysis (PDA) of functional data for obtaining a low-dimensional representation of a collection of curves. PDA assumes there exists a linear differential operator that results in the zero-function when it is applied to each of the data curves, or equivalently, that the curves belong to a low-dimensional subspace of a normed linear space. PDA sets out to estimate this linear differential operator from the data and proceeds from there. Our contribution is to explain how subject covariates can be incorporated into a PDA analysis for graphical exploration of patterns in the data.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2015.1043387