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We consider a variable selection problem for functional linear models where both multiple predictors and a response are functions. We assume that these variables are given as functions of time and the...n construct the historical functional linear model which takes the dependence of time between multiple predictors and a response into consideration. Unknown parameters included in the model are estimated by the maximum penalized likelihood method with the L1-type penalty. We can simultaneously estimate and select variables given as functions owing to the sparsity penalty. A regularization parameter involved in the regularization method is decided by a model selection criterion. The effectiveness of the proposed method is investigated by simulation studies and real data analysis.show more
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Export Mendeley
BIC-53-1