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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Vydané v:	Journal of the American Statistical Association Ročník 112; číslo 518; s. 690 - 705
Hlavní autori:	Luo, Ruiyan, Qi, Xin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 01.06.2017 Taylor & Francis Group,LLC Taylor & Francis Ltd
Predmet:	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
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