We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments.
- Authors
Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E.
- Abstract
Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with <italic>x</italic>- and <italic>y</italic>-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.
- Subjects
TRILINEAR forms; FACTOR analysis; STATISTICAL correlation; MULTIVARIATE analysis; ELECTROENCEPHALOGRAPHY
- Publication
Psychometrika, 2018, Vol 83, Issue 1, p1
- ISSN
0033-3123
- Publication type
Article
- DOI
10.1007/s11336-017-9558-9