We introduce continuously additive models, which can be viewed as extensions of additive regression models with vector predictors to the case of infinite-dimensional predictors. This approach produces a class of flexible functional nonlinear regression models, where random predictor curves are...

In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are...

We consider the problem of estimating functional derivatives and gradients in the framework of a regression setting where one observes functional predictors and scalar responses. Derivatives are then defined as functional directional derivatives that indicate how changes in the predictor...