Fotografi av Martin Singull

Martin Singull

Professor, Avdelningschef

Presentation

En utförligare presentation finns på min engelska medarbetarsida

Uppdrag

  • Avdelningschef för Matematisk statistik vid Matematiska institutionen, Linköpings universitet
  • Biträdande föreståndare för Forskarskolan i tvärvetenskaplig matematik vid Linköpings universitet
  • Team Leader för delprogrammet Tillämpad matematik och statistik inom forskningsprogrammet University of Rwanda - Sweden Research, Higher Education and Institutional Advancement Cooperation Programme
  • Styrelseordförande för Cramér Society (2022-)

Nyheter

CV

Forskning

Doktorander

Publikationer

2024

Katarzyna Filipiak, Dietrich von Rosen, Martin Singull, Wojciech Rejchel (2024) Estimation under inequality constraints in univariate and multivariate linear models

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, s. 287-305 Vidare till 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, s. 3185-3202 Vidare till 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, s. 119-135 Vidare till DOI
Emelyne Umunoza Gasana, Dietrich von Rosen, Martin Singull (2022) Moments of the likelihood-based discriminant function Communications in Statistics - Theory and Methods Vidare till 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, Artikel 95 Vidare till 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 Vidare till DOI
Felix Wamono, Dietrich von Rosen, Martin Singull (2022) Residuals in GMANOVA-MANOVA model with rank restrictions on parameters Journal of the Korean Statistical Society, Vol. 51, s. 223-244 Vidare till DOI

2021

Hasifa Nampala, Matylda Jablonska-Sabuka, Martin Singull (2021) Mathematical Analysis of the Role of HIV/HBV Latency in Hepatocytes Journal of Applied Mathematics, Vol. 2021, Artikel 5525857 Vidare till DOI

Böcker

Recent Developments in Multivariate and Random Matrix Analysis

Omslaget till boken 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.

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