The Statistics Group

The first statistician at the NTH, Professor Arnljot Høyland, was appointed in 1965. He started both research and teaching in reliability analysis and methods for quality improvements and quality control.

The statistics group at NTNU is now by far the largest supplier of Master-level students in Norway. Much of the current research is motivated by challenges from other academic disciplines and real-world businesses.

Areas of research


Besides population dynamics this research also covers topics as evolutionary biology, population genetics, ecology, conservation biology and functional genomics.


An important activity is statistical modelling and analysis of data from genomics, where multiple hypothesis testing is a central research topic. Ongoing research also includes exact hypothesis testing concerning parameters of discrete distributions in the presence of nuisance parameters.

Industrial statistics

The main research topics include Design of Experiments (DOE), reliability analysis and extreme value statistics. In reliability, focus is modelling and statistical inference in connection with repairable and maintainable systems and calculation of system reliability of structural systems. In extreme value statistics, focus is estimation of extreme responses of dynamic structures and extreme value prediction from sampled time series. The research in DOE is directed towards projection properties of non-regular two-level designs.

Spatial statistics

A major topic is sampling algorithms for complex stochastic systems, in particular for Gaussian random fields and Gaussian Markov-random fields. The activity also covers spatial categorical variables of interest for seismic inversion, spatio-temporal models used for reservoir characterization and tools for computing the Value of Information in spatial models.

Computational statistics

Research is directed towards speeding up algorithms for handling complex statistical problems. Special focus is given to Gaussian Markov random fields and applications of the approach INLA which makes it possible to avoid MCMC for doing Bayesian inference for latent Gaussian models.

Theoretical statistics

Topics studied are characteristic functions and choice of smoothing parameters in kernel density estimation and methods for Monte Carlo computation of conditional distributions given sufficient statistics.