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2020 – today
- 2024
[j20]Mark de Rooij
, Frank M. T. A. Busing:
Multinomial Restricted Unfolding. J. Classif. 41(1): 190-213 (2024)- [j19]Wouter van Loon, Marjolein Fokkema, Frank de Vos, Marisa Koini, Reinhold Schmidt, Mark de Rooij:
Imputation of missing values in multi-view data. Inf. Fusion 111: 102524 (2024) - 2023
- [j18]Bunga C. Pratiwi, Elise Dusseldorp, Julian D. Karch, Mark de Rooij:
Predictive performance of psychological tests: Is it better to use items than subscales? Comput. Stat. Data Anal. 185: 107767 (2023) - [i5]Kevin Kloos, Julian D. Karch, Quinten A. Meertens, Mark de Rooij:
Continuous Sweep: an improved, binary quantifier. CoRR abs/2308.08387 (2023) - 2022
- [j17]Tiffany Elsten, Mark de Rooij:
SUBiNN: a stacked uni- and bivariate kNN sparse ensemble. Adv. Data Anal. Classif. 16(4): 847-874 (2022) - [j16]Beibei Yuan, Willem J. Heiser, Mark de Rooij:
A comparison of two dissimilarity functions for mixed-type predictor variables in the δ-machine. Adv. Data Anal. Classif. 16(4): 875-907 (2022) - [i4]Wouter van Loon, Marjolein Fokkema, Mark de Rooij:
Imputation of missing values in multi-view data. CoRR abs/2210.14484 (2022) - 2021
- [i3]Wouter van Loon, Frank de Vos, Marjolein Fokkema, Botond Szabó, Marisa Koini, Reinhold Schmidt, Mark de Rooij:
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification. CoRR abs/2108.05761 (2021) - 2020
- [j15]Wouter van Loon, Marjolein Fokkema, Botond Szabó, Mark de Rooij:
Stacked penalized logistic regression for selecting views in multi-view learning. Inf. Fusion 61: 113-123 (2020) - [i2]Wouter van Loon, Marjolein Fokkema, Botond Szabó, Mark de Rooij:
View selection in multi-view stacking: Choosing the meta-learner. CoRR abs/2010.16271 (2020)
2010 – 2019
- 2019
- [j14]Beibei Yuan, Willem J. Heiser, Mark de Rooij:
The δ-Machine: Classification Based on Distances Towards Prototypes. J. Classif. 36(3): 442-470 (2019) - 2018
- [j13]Mark de Rooij:
Transitional modeling of experimental longitudinal data with missing values. Adv. Data Anal. Classif. 12(1): 107-130 (2018) - [j12]Hailemichael M. Worku, Mark de Rooij:
A Multivariate Logistic Distance Model for the Analysis of Multiple Binary Responses. J. Classif. 35(1): 124-146 (2018) - [j11]Frank de Vos, Marisa Koini, Tijn M. Schouten, Stephan Seiler, Jeroen van der Grond, Anita Lechner, Reinhold Schmidt, Mark de Rooij, Serge A. R. B. Rombouts:
A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease. NeuroImage 167: 62-72 (2018) - [i1]Wouter van Loon, Marjolein Fokkema, Botond Szabó, Mark de Rooij:
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning. CoRR abs/1811.02316 (2018) - 2017
- [j10]Tijn M. Schouten, Marisa Koini, Frank de Vos, Stephan Seiler, Mark de Rooij, Anita Lechner, Reinhold Schmidt, Martijn P. van den Heuvel, Jeroen van der Grond, Serge A. R. B. Rombouts:
Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging. NeuroImage 152: 476-481 (2017) - 2015
- [j9]Najmeh Khalili-Mahani, Marieke Niesters, Matthias J. P. van Osch, Melly S. Oitzl, Ilya M. Veer, Mark de Rooij, Joop M. A. van Gerven, Mark A. van Buchem, Christian F. Beckmann, Serge A. R. B. Rombouts, Albert Dahan:
Ketamine interactions with biomarkers of stress: A randomized placebo-controlled repeated measures resting-state fMRI and PCASL pilot study in healthy men. NeuroImage 108: 396-409 (2015) - 2013
- [j8]Hsiu-Ting Yu, Mark de Rooij:
Model Selection for the Trend Vector Model. J. Classif. 30(3): 338-369 (2013) - 2012
- [j7]Mark de Rooij, Hailemichael M. Worku:
A warning concerning the estimation of multinomial logistic models with correlated responses in SAS. Comput. Methods Programs Biomed. 107(2): 341-346 (2012)
2000 – 2009
- 2009
- [j6]Frank M. T. A. Busing, Mark de Rooij:
Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings. J. Classif. 26(3): 329-360 (2009) - [j5]Marike Polak, Willem J. Heiser, Mark de Rooij:
Two types of single-peaked data: Correspondence analysis as an alternative to principal component analysis. Comput. Stat. Data Anal. 53(8): 3117-3128 (2009) - [j4]Mark de Rooij:
Trend vector models for the analysis of change in continuous time for multiple groups. Comput. Stat. Data Anal. 53(8): 3209-3216 (2009) - 2003
- [j3]John C. Gower, Mark de Rooij:
A Comparison of the Multidimensional Scaling of Triadic and Dyadic Distances. J. Classif. 20(1): 115-136 (2003) - [j2]Mark de Rooij, John C. Gower:
Maximum Split Clustering Under Connectivity Constraints. J. Classif. 20(2): 181-220 (2003) - 2002
- [j1]Mark de Rooij:
Distance Models for Three-Way Tables and Three-Way Association. J. Classif. 19(1): 161-178 (2002)
Coauthor Index
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