Department of Statistics

Lund University School of Economics and Management


The Department of Statistics conducts research in several areas of statistics. These areas are presented below. Please visit the researchers´ personal presentations for more detailed information. 


Financial Statistics

In financial statistics studies are conducted of optimal investment strategies under constraints and of dependencies between GARCH processes. In our studies we also extend beyond the Gaussian paradigm of error distribution in the financial and economic data models. The models are used to investigate the driving forces of market risks.

Active researchers: Krzysztof Podgorski Krzysztof Nowicki, Farrukh Javed, Aleksey Kolokolov, Stepan MazurNima Shariati Fokalaei

Stochastic Networks

The department’s research in stochastic networks is focused on statistical models for social networks.

Active researchers: Krzysztof Nowicki

Multivariate Analysis

Part of the research in this field focuses on sample spaces with constraints, for instance correlation structures on simplices and models for circular data. Another part focuses on multivariate generalised linear models and multilevel models.

Active researchers: Björn Holmquist, Jakob Bergman, Peter Gustafsson, Sultana NasrinAnna Sjöström


The department carries out applied research in biostatistics in collaboration with, inter alia, neurosurgeons, psychiatrists, paediatricians and dentists. It also conducts theoretical biostatistical research in the theory of exact tests, and on models for survival data and for ordinal data.

Active researchers: Per-Erik IsbergHolger Kraiczi, Vibeke Horstmann

Applied Research in Social Sciences

The department also conducts applied research in collaboration with for example the Department of Social and Economic Geography, the Department of Sociology, the Department of Economic History, and the Division of Sociology of Law.

Active researchers: Björn Holmquist, Pierre Carbonnier, Per-Erik Isberg, Sultana NasrinJakob Bergman

Stochastic Models and Computational Statistics

Spatial-temporal random fields with focus on alternatives to traditional linear models and Gaussian distributions. Extreme events analysis, stochastic geometry and dynamical extensions of the models. Applications for environmental sciences, engineering as well as for economic and biomedical data. Implementation of computationally intensive methods in non-standard settings: the Expectation and Maximization algorithm, statistical bootstrap and other resampling methods, Monte Carlo Markov Chains.

Active researchers: Krzysztof Podgorski, Farrukh Javed, Peter GustafssonNima Shariati Fokalaei


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