Mark De Rooij

Professor

Leiden University

Netherlands

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About

Mark De Rooij is a Professor at Universiteit Leiden in the Netherlands. His research areas include statistical modeling and analysis techniques, particularly in mixed predictor and response variables, multilevel latent variable models, and binary data analysis. Recent publications highlight his work on reduced rank regression, logistic multidimensional data analysis, and the evaluation of students' proficiency.

Articles (16)

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2025

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2025

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2024

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2024

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2023

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2022

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2022

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2021

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2020

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2020

Collaborators (7)

Marjolein Fokkema

Associate Professor at Leiden University

Leiden University

NETHERLANDS

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Tom F. Wilderjans

Leiden University

NETHERLANDS

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José Fernando Vera Vera

Prof. Dr.

Universidad de Granada

SPAIN

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Francesco Palumbo

Full Professor of Statistics

University of Naples Federico II

ITALY

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W. D. Weeda

Associate Professor

Leiden University

NETHERLANDS

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Julian D. Karch

Leiden University

NETHERLANDS

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Botond Szabo

Associate Professor

Bocconi University

ITALY

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Mark De Rooij

Research Interests

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About

Articles (16)

Reduced rank regression for mixed predictor and response variables

Supervised and unsupervised mapping of binary variables: a proximity perspective

Sparse constrained and unconstrained non-symmetric correspondence analysis

Logistic Multidimensional Data Analysis for Ordinal Response Variables Using a Cumulative Link Function

A multinomial canonical decomposition model, with emphasis on the analysis of multivariate binary data

Students' proficiency evaluation: a non-parametric multilevel latent variable model approach

View selection in multi-view stacking: choosing the meta-learner

Multinomial Restricted Unfolding

A new algorithm and a discussion about visualization for logistic reduced rank regression

Longitudinal determinants of employment status in people with relapsing-remitting multiple sclerosis

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study

SEM-Based Out-of-Sample Predictions

SUBiNN: a stacked uni- and bivariate kNN sparse ensemble

A comparison of two dissimilarity functions for mixed-type predictor variables in the δ-machine

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

Cross-Validation: A Method Every Psychologist Should Know

Collaborators (7)

Marjolein Fokkema

Tom F. Wilderjans

José Fernando Vera Vera

Francesco Palumbo

W. D. Weeda

Julian D. Karch

Botond Szabo

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