Function-on-Partially Linear Functional Additive Models

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing them to have either a linear or nonlinear effect on the response. However, the assignment of features to the linear and nonlinear groups is typically assumed known. Thus, to make a GPLAM a viable approach in situatio...

Variance function partially linear single-index models

We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model.We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model.We develop efficient and practical estimatio...

Linear Models for Functional Data

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Functional wavelet regression for linear function-on-function models

1 Appendix B: Additional proofs of theorems . . . . . . . . . . . . . . . 2 1.1 Proof of Theorem 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Proof of Theorem 4.3 . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 Appendix D: Proofs of technical lemmas . . . . . . . . . . . . . . . . . 21 3 Appendix C: Additional simulation . . . . . . . . . . . . . . . . . . . . 44 4 Appendi...

Partially Linear Models