Function-on-Function Linear Regression by Signal Compression

We consider functional linear regression models with a functional response and multiple functional predictors, with the goal of finding the best finite-dimensional approximation to the signal part of the response function. Defining the integrated squared correlation coefficient between a random vari...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 112; H. 518; S. 690 - 705
Hauptverfasser:	Luo, Ruiyan, Qi, Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 01.06.2017 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	Americans Compression Convergence Correlation coefficients Eigenvalues Finite-dimensional approximation to coefficient kernel function Function Function-on-function linear regression functional response models Integrated squared correlation coefficient Integrated squared covariance least squares linear models objectives Penalized generalized functional eigenvalue problem Performance prediction Projections Random variables Regression analysis Regression models Response functions Scalarity (Semantics) Signal compression Simulation Statistics Theory and Methods
ISSN:	0162-1459, 1537-274X, 1537-274X
Online-Zugang:	Volltext
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