Research in mathematical statistics aims at general ideas and methods applicable in a wide range of areas in science, industry and society. Much of the current research is motivated by challenges from other academic disciplines and real-world businesses.
We have research groups developing stochastic models and statistical methods for analysis of spatiotemporal data, functional data, and high-dimensional genomics data. Our research links naturally to research activities at campus, including CLiC, CMTF, IceLab and UMIT, and also at our neighbour university SLU. We have access to advanced infrastructure at HPC2N and resources at BILS and SciLifeLab. Umeå University has a strong tradition in empirical sciences and big data are produced and analysed within several research units at campus including UCMR, UPSC, Radiation Sciences, and Cloud and Grid Computing.
Present research areas in mathematical statistics can be shortly described by the following two lists of keywords:
Classification and cluster analysis Computer intensive methods such as bootstrap and subsampling Functional data analysis Hidden Markov models Marked spatial point processes Maximum spacing methods Non-parametric statistics, nearest neighbour methods Numerical analysis and simulation of random functions Quantization of random processes and sequences Spatial statistics and statistical imaging Statistical learning Time series analysis and statistical signal processing, time-frequency analysis
Biostatistics and bioinformatics Biomedical engineering Compressive sensing Computational statistics Digital signal processing for audio conferencing systems Environmetrics Forest science Human movement analysis Limnology Medical imaging Population biology Remote sensing and environment Skeletal muscle characterisation Sport science Wavelets in signal and image analysis