Research areas cover both theoretical and applied statistics as well as machine learning. This includes inference for high-dimensional data, survival and event history analysis, model selection and criticism, graphical modelling, non-parametrics, machine learning, hierarchical Bayesian modelling, Bayesian neural networks, time- and space-modelling, and general methodological development motivated from applications in public health, genetics, biology, climate science and other fields.
This specialization is mainly driven by the research group in Statistics and Data Science and potential supervisors include the staff within this group. There are also many possibilities for external supervision through our many collaborators.
Some recent master projects:
- The application of penalized logistic regression for fraud detection by Shuijing Liao. Building good prediction models for detecting the fraudulent cases faces challenges, for instance, due to inappropriate measures of prediction performance. We study, in the setting of fraud detection, whether and how the prediction performance of a penalized logistic regression model may be improved by applying appropriate optimality measures in cross-validation of the penalty parameters.
- Electricity Demand Forecasting by Eirik Sjåvik. This thesis introduces a medium-term forecast model for electricity demand in the Nordic region utilizing seasonal Numerical Weather Prediction temperature forecasts
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Modelling Short Term Changes in User Interest for Online Marketplaces by Øystein Skauli: This thesis analyse the evolution of interest in recommendation systems using hidden Markov models.