Statistical Inference on Latent Structures from Sequential Data (SILAS)

Sequential data, i.e., observations made over time, enable valuable inference on underlying latent structures in many empirical settings, such as animal movement measures, medical and psychological data, social media activities or sports athletes’ performances. The research initiative SILAS aims to facilitate the transfer and integration of statistical models and methods for analysing such increasingly complex data across disciplines. By bringing together statisticians working in a variety of applied fields, it combines a wide range of methodological expertise and empirical experience from ares such as medicine, ecology, sociology, psychology and economics.