Gabriel Wallin webpage

Gabriel Wallin

Email: gabriel.wallin[at]

I am a postdoctoral researcher at Université Côte d’Azur in Nice, where I am part of the Maasai (Models and Algorithms for Artificial Intelligence) team of Inria and the J. A. Dieudonné laboratory, which is the department of mathematics. I work with Professors Charles Bouveyron and Elena Erosheva.

Prior to joining the Maasai team, I did my PhD at the Department of Statistics at Umeå University under the supervision of Professor Marie Wiberg. I defended my PhD thesis in January 2020.

My research interests include:

  • Model-based clustering and co-clustering,
  • Statistical analysis of massive longitudinal data,
  • Psychometric methods and models.

I use the methods that I develop to answer questions within the fields of medicine, sociology and educational measurement.


Wallin, G. and Wiberg, M. (2019). Kernel Equating Using Propensity Scores for Nonequivalent Groups. Journal of Educational and Behavioral Statistics, 44, 390-414.

Wallin, G., Häggström, J. and Wiberg, M. (2018). How to Select the Bandwidth in Kernel Equating. In Wiberg, M., Culpepper, S., Janssen, R., González, J. and Molenaar, D., Quantitative Psychology. Springer, Cham.

Wallin, G. and Wiberg, M. (2017). Nonequivalent groups with covariates design using propensity scores for kernel equating. In Ark, A. L., Wiberg, M., Culpepper, S. A., Douglas, J. A. and Wang, W-C., Quantitative Psychology, Springer, Cham.

Working papers

Wallin, G. and González, J. (2020). Revisiting the Bahadur Representation of Sample Quantiles for the Standard Error of Kernel Equating.

Wallin, G. and Wiberg, M. (2020). Model Selection for Presmoothing of Bivariate Score Distributions in Kernel Equating.

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