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Mathematical Statistics

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.

Research expertise

Present research areas in mathematical statistics can be shortly described by the following two lists of keywords:

Theory 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

Inter-disciplinary keywords:

Biostatistics and bioinformatics
Biomedical engineering
Compressive sensing
Computational statistics
Digital signal processing for audio conferencing systems
Forest science
Human movement analysis
Medical imaging
Population biology
Remote sensing and environment
Skeletal muscle characterisation
Sport science
Wavelets in signal and image analysis


Patrik Rydén
Other position, associate professor
Jun Yu