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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.

About me

Academic Degrees (Linköping University)

  • Docent in Mathematical Statistics, March 2016
  • Doctor of Philosophy PhD (Teknologie doktor), Mathematical Statistics, 2009
  • Licentiate of Engineering (Teknologie licentiat), Mathematical Statistics, 2007
  • Master of Science in Applied Physics and Electrical Engineering, 2003

Academic Experience (Linköping University)

  • Head of Division – Applied Mathematics, 2021 -
  • Professor in Mathematical Statistics, 2020
  • Head of Division – Mathematical Statistics, 2016 - 2020
  • Associate Professor in Mathematical Statistics, 2018
  • Assistant Professor in Mathematical Statistics, 2012

Commissions of Trust

  • Director for the Research School in Interdisciplinary Mathematics at Linköping University, 2024 -
  • Expert group member (Technology) STINT, The Swedish Foundation for International Cooperation in Research and Higher Education, 2022 -
  • Chair of the board of Cramérsällskapet (part of the Swedish Statistical Society), 2022 - 2024
  • 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 (ended 2022)
  • Member of ISP (International Science Programme) Mathematics Reference Group, 2021 -
  • Board member of the Faculty of Science and Engineering, Linköping University, 2021-
  • Member of the international scientific committee for the conference series International Workshop on Matrices and Statistics (IWMS), 2019 -
  • Chair of organizing committee International Workshop on Matrices and Statistics (IWMS) at Linköping University, 2023
  • Chair of organizing committee for the International Conference on Trends and Perspectives in Linear Statistical Inference (LinStat2014) at Linköping University, 2014

PhD students

Other PhD students

  • Jean de Dieu Niyigena (University of Rwanda)
    Project title: Approximation of misclassification probabilities using quadratic classifier for repeated measurements (main supervisor)
  • Moa Eriksson (BKV, Linköping University)
    Preliminary project title: The impact of internal and external factors on the eye with a focus on retinal diseases (co-supervisor)
  • John Södling (HMV, Linköping University)
    Preliminary project title: Ischemic Heart Disease, Socioeconomic Impact, and Prediction Models in Young Cancer Patients (co-supervisor)

Former PhD students

Research

Abstract 3D pattern.

Modern Multivariate Statistical Analysis

Nowadays there is a great need to analyse complex high-dimensional data. Modern theories must be developed through the knowledge of the classical methods of multivariate statistics.

A group of people holding in a jigsaw

Research School in Interdisciplinary Mathematics

The Department of Mathematics offers a doctoral programme for students with a strong mathematical interest who are interested to apply mathematics on practical problems in different fields such as engineering, business, computer science and industry.

Flags of countries taking part in the research collaboration.

Research collaboration in Mathematics with low income countries and regions

The Department of Mathematics contributes to the development of capacity for higher education and research in low income countries and regions through collaborative projects in Africa and Asia.

Group members involved in the sub-programme Strengthening Research Capacity in Mathematics, Statistics and Their Applications.

Research collaboration with the University of Rwanda in Applied Mathematics and Statistics

Our goal is to strengthen research and postgraduate training in mathematics, statistics and their applications at the University of Rwanda, and to promote the use of mathematics and statistics in Rwanda and in the region. Funded by Sida.

Meeting at the Royal University of Phnom Penh in 2019.

Research Collaboration with the Royal University of Phnom Penh in Mathematics and Statistics

Our goal is to contribute to the development of Cambodia by increasing the knowledge in Mathematics and Statistics and their use in the private and public sectors and by conducting research in areas relevant to the country.

Image in sketched format with human hearts getting treatment and a teddybear sitting next to it

Computational Cardio-Oncology

Many pediatric cancer care survivors develop serious cardiovascular complications later in life. The emerging field of computational cardio-oncology leverages advanced data methods to better predict and prevent these complications.

News

Publications

2024

Jean de Dieu Niyigena, Innocent Ngaruye, Joseph Nzabanita, Martin Singull (2024) Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices
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 (Article in journal) 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 (Article in journal) Continue to DOI
Katarzyna Filipiak, Dietrich von Rosen, Martin Singull, Wojciech Rejchel (2024) Estimation under inequality constraints in univariate and multivariate linear models
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2024) Moments of the likelihood-based discriminant function Communications in Statistics - Theory and Methods, Vol. 53, p. 1122-1134 (Article in journal) Continue to DOI

2023

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 (Chapter in book) 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 (Article in journal) Continue to DOI

2022

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 (Chapter in book) 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 (Article in journal) Continue to DOI

Books

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

Conference

Organisation