Contact person: Ingrid Hobæk Haff
Keywords: Parametric models, explainable models, algorithms, conflict, sensitivity, hierarchical models
Research group: Statistics and Data Science
Department of Mathematics
There is currently an exponential growth of data collected in different areas. Data sources can both be huge and of different types (e.g. images, text, videos, table data, knowledge graphs). Although blackbox machine learning techniques may be applicable for modeling in some settings, more direct use of parametric modeling will be useful when knowledge about the problem and data sources are available. Further, parametric models give a more direct route towards explainable models. Traditional statistical models may however not be directly applicable to the new data modalities. Developing new models or adapting existing ones to appropriately handle the specific structures and assumptions of these data is necessary. Additionally, conducting statistical inference, model checking and hypothesis testing with the new data modalities may require innovative approaches. Potential applications are within ecology, healthcare, environmental applications, industry and technology.
Methodological research topics:
- Combining data of different modalities through hierarchical/graphical structured modelling
- Complex dependencies in high dimension through copula modelling
- Handling high-dimensional data with regularisation or dimension reduction
- Event history analysis using boosting techniques
- Efficient and scalable algorithms for statistical inference
- Spatio-temporal modelling towards massive ecological data
External partners:
- Norwegian Computing Center (NR)