Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 93, Issue -, Pages 86-96Publisher
ELSEVIER
DOI: 10.1016/j.csda.2014.11.017
Keywords
EM algorithm; Functional data analysis; Lasso; Wavelets
Funding
- NIBIB grant [5 R01 EB009744]
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Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the differing associations between those predictors and responses. The classical finite mixture regression model is extended to incorporate functional predictors by taking a wavelet-based approach in which both the functional predictors and the component-specific coefficient functions are represented in terms of an appropriate wavelet basis. By using the wavelet representation of the model, the coefficients corresponding to the functional covariates become the predictors. In this setting, there are typically many more predictors than observations. Hence a lasso-type penalization is employed to simultaneously perform feature selection and estimation. Specification of the model is discussed and a fitting algorithm is provided. The wavelet-based approach is evaluated on synthetic data as well as applied to a real data set from a study of the relationship between cognitive ability and diffusion tensor imaging measures in subjects with multiple sclerosis. (C) 2014 Elsevier B.V. All rights reserved.
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