A fundamental task in the analysis of data sets with many variables is screening for associations. This can be cast as a multiple testing task, where the objective is achieving high detection power while controlling type I error. We consider m hypothesis tests represented by pairs...

Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome. Such ‘points of impact’ are typically unknown and...

We develop new multiscale methods to test qualitative hypotheses about the function m in the non-parametric regression model Yt,T=m(t/T)+ɛt with time series errors ɛt. In time series applications, m represents a non-parametric time trend. Practitioners are often interested in whether the trend...