  Sparsity-inducing penalties are useful tools for variable selection and they
are also effective for regression settings where the data are functions. We
consider the problem of selecting not only variables but also decision
boundaries in logistic regression models for functional data, using the sparse
regularization. The functional logistic regression model is estimated by the
framework of the penalized likelihood method with the sparse group lasso-type
penalty, and then tuning parameters are selected using the model selection
criterion. The effectiveness of the proposed method is investigated through
real data analysis.
