Photo of Martin Singull

Martin Singull

Professor, Head of Division

Everywhere and all the time, huge amounts of data are generated that must be analyzed to be useful for the development of society. These data sets are large in that sense, that they may include many independent or dependent observations. They can also be more complex, with, for example, repeated measurements on the same units or individuals.

Professor Martin Singull's research focuses on statistical inference of this type of data. In particular, the considered statistical models contain unknown parameters, which describe the expected values over time as well as all dependencies. The parameters need to be estimated so that the statistical models can be further used for prediction and classification of future unknown observations.

In his latest research projects, Martin Singull uses both the spatial and temporal information in repeated measurements, to develop efficient classifiers, and derive probabilities of misclassification, for future unknown units or individuals that are assumed to follow different expected values over time.

Professional activities

  • Head of Division of Applied Mathematics at the Department of Mathematics, Linköping University
  • Assistant Director for the Research School in Interdisciplinary Mathematics at Linköping University
  • Team Leader for the Sida funded bilateral subprograms in mathematics and statistics at the five universities Royal University of Phnom Penh (Cambodia), Eduardo Mondlane University (Mozambique), University of Rwanda, University of Dar Es Salaam (Tanzania) and Makerere University (Uganda)
  • Chair of the Board of the Cramér Society (2022- )




PhD students



Margaretha Stenmarker, Panagiotis Mallios, Elham Hedayati, Kenny A. Rodriguez-Wallberg, Aina Johnsson, Joakim Alfredsson, Bertil Ekman, Karin Garming Legert, Maria Borland, Johan Mellergård, Moa Eriksson, Ina Marteinsdottir, Thomas Davidson, Lars Engerström, Malte Sandsveden, Robin Keskisärkkä, Martin Singull, Laila Hübbert (2024) Morbidity and mortality among children, adolescents, and young adults with cancer over six decades: a Swedish population-based cohort study (the Rebuc study) The Lancet Regional Health: Europe, Vol. 42, Article 100925 Continue to DOI
Dietrich von Rosen, Martin Singull (2024) Using the growth curve model in classification of repeated measurements Annals of the Institute of Statistical Mathematics, Vol. 76, p. 511-534 Continue to DOI
Katarzyna Filipiak, Dietrich von Rosen, Martin Singull, Wojciech Rejchel (2024) Estimation under inequality constraints in univariate and multivariate linear models


Béatrice Byukusenge, Dietrich von Rosen, Martin Singull (2023) On Residual Analysis in the GMANOVA-MANOVA Model Trends in Mathematical, Information and Data Sciences: A Tribute to Leandro Pardo, p. 287-305 Continue to DOI
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2023) Moments of the Likelihood-based Classification Function using Growth Curves
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2023) Edgeworth-type expansion of the density of the classifier when growth curves are classified via likelihood
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2023) An Edgeworth-type expansion for the distribution of a likelihood-based discriminant function Journal of Statistical Computation and Simulation, Vol. 93, p. 3185-3202 Continue to DOI


Béatrice Byukusenge, Dietrich von Rosen, Martin Singull (2022) On the Identification of Extreme Elements in a Residual for the GMANOVA-MANOVA Model Innovations in Multivariate Statistical Modeling: Navigating Theoretical and Multidisciplinary Domains, p. 119-135 Continue to DOI
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2022) Moments of the likelihood-based discriminant function Communications in Statistics - Theory and Methods Continue to DOI
Felix Wamano, Leonard Atuhaire, Innocent Ngaruye, Dietrich von Rosen, Martin Singull (2022) Estimation of trends in household living standards in Uganda using a GMANOVA-MANOVA model with rank restrictions
Dietrich von Rosen, Martin Singull (2022) Classification of repeated measurements using growth curves
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2022) Approximated misclassification errors for the likelihood based discriminant function via Edgetworth-type expansion
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2022) The first two cumulants of the (quadratic) likelihood-based discriminant functions
Pontus Söderbäck, Jörgen Blomvall, Martin Singull (2022) Improved Dividend Estimation from Intraday Quotes Entropy, Vol. 24, Article 95 Continue to DOI
Béatrice Byukusenge, Dietrich von Rosen, Martin Singull (2022) On an Important Residual in the GMANOVA-MANOVA Model Journal of Statistical Theory and Practice, Vol. 16 Continue to DOI


Recent Developments in Multivariate and Random Matrix Analysis

The cover of Recent Developments in Multivariate and Random Matrix Analysis.Festschrift in Honour of Dietrich von Rosen
Edited by Thomas Holgersson and Martin Singull

This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday. It contains a collection of twenty original papers. The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models.

More about the book