Continuously Additive Models for Nonlinear Functional Regression

  title={Continuously Additive Models for Nonlinear Functional Regression},
  author={Hans-Georg M{\"u}ller and Yichao Wu},
We introduce continuously additive models, which can be motivated as extensions of additive regression models with vector predictors to the case of infinite-dimensional predictors. This approach provides a class of flexible functional nonlinear regression models, where random predictor curves are coupled with scalar responses. In continuously additive modeling, integrals taken over a smooth surface along graphs of predictor functions relate the predictors to the responses in a nonlinear fashion… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 12 extracted citations

Similar Papers

Loading similar papers…