Directional data are data that represent directions, e.g. the direction of wind or the direction in
which an animal moves. These data are represented as points on the unit circle or unit sphere, since
they have no "length". This renders their statistical treatment very delicate, as for instance the
simple average of two points on the sphere will result in a point lying inside the sphere and hence
not on the sphere. The ...
Affect research relies on intensive longitudinal data nowadays to understand the dynamics underneath affective functioning. To unravel them, vector autoregressive (VAR) models, in which affective states are regressed upon each other over time, have become especially popular. Theoretical work, however, emphasizes the notion of regulatory flexibility, suggesting that affective dynamics depend on and thus change with context. To empirically ...
There is a recent awareness of requiring additional efforts for inference when a selection of variables or models has taken place. This can be placed into the larger framework of correct inference and research integrity.
We focus mainly on the high-dimensional setting and consider these valid post-selection issues for composite estimation, model averaging and dimension reduction. Composite estimation involves using a linear combination ...
Programmer productivity is key in today's software development: broader functionality must be provided on more projects, in less time and with fewer developers. Yet, productivity often suffers because programmers structure and reason about their programs in an ad-hoc manner.
This project aims to remedy the situation by applying concepts from category theory, an abstract branch of mathematics, to systematically structure programs. More ...
Gaining competitive advantage requires today’s businesses and industries to innovate and improve at an increasing speed. Modern products, production processes and services are more complex than ever, so optimizing product formulations/compositions, production process settings and services requires solving high-dimensional decision problems. An important management science tool to cope with this task is design of experiments. A design is a ...