Fotografi av Martin Singull

Martin Singull

Professor, Avdelningschef

Presentation

En utförligare presentation finns på min engelska medarbetarsida

Nyheter

Forskning

Doktorander

Publikationer

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, Artikel 100925 (Artikel i tidskrift) Vidare till 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, s. 511-534 (Artikel i tidskrift) Vidare till 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, s. 1122-1134 (Artikel i tidskrift) Vidare till 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, s. 287-305 (Kapitel i bok, del av antologi) 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 (Artikel i tidskrift) 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 (Kapitel i bok, del av antologi) 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 (Artikel i tidskrift) 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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