Johan Henrik Koskinen
About me
I am Lecturer in Statistics at Stockholm University. After obtaining my degree in the department, I worked at the Universities of Melbourne, Oxford, Manchester, and Linkoping, before returning to my alma mater.
Teaching
This semester I am teaching Multivariate Methods and GLM. I have supervised a number of master’s and PhD students in various areas of applied statistics, including measurement error in labour force participation, survival analysis in Criminology, and social network analysis applied in a variety of areas. I am always looking for research students interested in advancing modelling and inference in statistical network analysis.
Research
My main research interests centre on statistical modelling and Bayesian inference for networks. Networks present massively interdependent binary (mostly) data, either cross-sectionally or longitudinally, and methodology for network analysis has evolved to become a thriving field of statistics.
Representative Publications
Koskinen, J.H. & Snijders, T.A.B. (2023). Multilevel longitudinal analysis of social networks. Journal of the Royal Statistical Society Series A, Vol. 186 (3), 376–400 (arXiv preprint arXiv:2201.12713) https://doi.org/10.1093/jrsssa/qnac009
Koskinen, J., and Daraganova, G. (2022). Bayesian Analysis of Social Influence. Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 185 (4) 1469--2325. https://doi.org/10.1111/rssa.12844
Stys, P., Verweijen, J., Muzuri, P., Muhindo, S., Vogel,C., and Koskinen, J. (2020) Brokering Between (not so) Overt and (not so) Covert Networks in Conflict Zones, Global Crime, 21(1): 74-110. DOI: 10.1080/17440572.2019.1596806
Bright, D , Koskinen, J., Malm, A. (2019). Illicit network dynamics: The formation and evolution of a drug trafficking network, Journal of Quantitative Criminology, 35(2): 237-258. DOI: 10.1007/s10940-018-9379-8
Koskinen, J., Wang, P., Robins, G., Pattison, P. (2018). Outliers and Influential Observations in Exponential Random Graph Models. Psychometrika, 83(4), 809-830. DOI: 10.1007/s11336-018-9635-8
Koskinen J., Caimo, A., Lomi, A. (2015). Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments. Network Science, 3(1): 58-77. DOI: 10.1017/nws.2015.3
Koskinen, J. H., Robins, G. L., Wang, P., Pattison, P. E. (2013). Bayesian analysis for partially observed network data, missing ties, attributes and actors. Social Networks, vol. 35(4), 514-527. DOI: 10.1016/j.socnet.2013.07.003
Lusher, D., Koskinen, J., Robins, G., (2013). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, Cambridge University Press, NY.
Snijders, T.A.B., Koskinen, J.H., & Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, Vol. 4(2), 567–588. DOI:10.1214/09-AOAS313
Koskinen, J.H. & Snijders, T.A.B. (2007). Bayesian Inference for Dynamic Social Network Data. Journal of Statistical Planning and Inference, Vol 137 (12) 3930-3938
Grants
Covert Networks: How to learn as much as possible about the structure of a network from sampled subnetworks (Grant W911NF-21-1-0335 for Proposal 79034-NS), Army Research Office [co-investigator; US lead M. Schweinberger]
Multidimensional networks of users and car purchases (Proposal No 2005661), National Science Foundation US [Named non-US partner]
Current Research Students
Jonathan Januar, Missing Data Mechanisms in Covert Networks [funded by W911NF-21-1-0335]