Mathematical Statistics

The group conducts research developing stochastic models and statistical methods. The topics of interest are

Bayesian statistics.
Spatial and spatio-temporal statistics, stochastic geometry and imaging, with applications towards demography, forestry, offshore operations and wave modelling for sea transports, materials science, neurology, telecommunication.
Theory for extremes in random processes and fields, and statistical analysis of extreme values such as extreme climatic episodes.
Reliability improvement: in particular development of load models and fatigue accumulation for trucks.
Financial risk management: research aimed at new methods for optimization of financial portfolios, measurement of catastrophic risks and handling of credit risks.
Statistical estimation and algorithms for non-stationary and non-Gaussian processes.
Computer intensive statistical methods and Big data statistics.
Environmental statistics.
Forensic statistics.

For life science statistics see the webpage of the research group in Biomathematics and biostatistics.
Our research in various ways intersects with other research groups at the Department, in particular with the groups in Computational Mathematics, Probability theory, and Optimization.

Twitter account of the Statistics group: follow it for news on statistics seminars, job openings in Statistics and more.

Seminars

Please visit the Statistics seminar which is also announced in the Calendar of Mathematical Sciences.

Members

Faculty
Patrik Albin	Extreme values
Marina Axelson-Fisk	Bioinformatics
Ottmar Cronie	Spatial and spatio-temporal statistics, statistical learning
Olle Häggström	Bayesian statistics and risk analysis
Rebecka Jörnsten	Biostatistics
Erik Kristiansson	Biostatistics
Petter Mostad	Bayesian statistics and forensics
Mike Pereira	Spatial statistics, SPDEs
Umberto Picchini	Bayesian inference and likelihood-free methods for stochastic modelling
Holger Rootzén	Extreme values and risk analysis
Serik Sagitov	Branching and coalescent processes
Moritz Schauer	Inference for stochastic differential equations, non-parametric Bayes
Aila Särkkä	Spatial statistics
Sergei Zuyev	Spatial statistics

Emeriti
Peter Jagers	Population dynamics, point processes
Olle Nerman	Biostatistics
Mats Rudemo	Spatial statistics and image analysis
Igor Rychlik	Ocean engineering modelling
Nanny Wermuth	Multivariate statistical models and their properties

PhD Students
Oskar Allerbo	Biostatistics
Oskar Eklund	Stochastic continuous-depth neural networks
Felix Held	Statistical methods for description and estimation of undirected graphs
Henrik Imberg	Sampling theory
Juan Inda	Multi-omics data integration
Anton Johansson	Deep learning, model selection for neural networks
Petar Jovanovski	Simulation-based Bayesian inference and deep learning for stochastic modelling
Konstantinos Konstantinou	Spatial statistics
David Lund	Large-scale analysis of DNA sequencing data
Helga Kristín Ólafsdóttir	Extreme value theory, non-Gaussian models within spatial statistics
Vincent Szolnoky	Neural networks and the implications of infinitely wide ones
Selma Tabakovic	Generalization bounds of neural networks

Page manager Published: Wed 09 Feb 2022.

Mathematical Statistics

Seminars

Members​

Members