The Benjamini-Hochberg (BH) procedure is a staple of modern high-dimensional data analysis. This method can be made more powerful by incorporating estimators of the number (or proportion) of null hypotheses, yielding an adaptive BH procedure which still controls the false discovery rate (FDR).

In this talk we present a unified class of estimators, which encompasses existing and new estimators and which can also be extended to discrete tests. While our focus is on presenting the generality and flexibility of the new class of estimators, we also include some analyses on simulated and real data.

Many methods for functional data require the data to be registered, that is, aligned in time. While this assumption simplifies the analysis, it cannot be justified in many applications, especially when working with continuously recorded sensor data, where no "start", "end" or any other landmark exists.

We introduce a novel dataset designed for benchmarking FDA methods, which includes eye-tracking data from over 100 participants, measured simultaneously via camera and EEG headset. The primary task is to reconstruct eye movements from EEG signals. The dataset contains different levels of difficulty (registered and unregistered data, continuous and abrupt movements, four and more directions of movement).

Additionally, we present a comparative analysis of functional neural networks, that are specifically designed for unregistered data, with established methods.

Wind power forecasting became a necessity following deployment of the first large-scale wind farms. Today, these forecasts are as important as ever and there are abundant opportunities for forecasts to unlock added value in settings ranging from maintenance scheduling to algorithmic trading. Despite advances in probabilistic forecasting, many operational systems struggle to incorporate uncertainty in a meaningful way, though this is changing. This talk will reflect on the evolution of wind power forecasting and the innovations that have led to improvements in forecast skill and forecast value. Drawing on a decade of work with wind farm operators, traders, TSOs and forecast vendors, I will present examples of how, with a little help, we can add value with wind power forecasts.