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Mark de Rooij
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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)
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last updated on 2024-07-04 22:21 CEST by the dblp team
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