Wavelet-Based Scalar-on-Function Finite Mixture Regression Models.

ABSTRACT: Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the di ering 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 selecti

SUBMITTER: Ciarleglio A 

PROVIDER: S-EPMC4620087 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature