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ORCIDJeroen K. Vermunt
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2020 – today
- 2025
[j20]S. Columbu, N. Piras, Jeroen K. Vermunt:
Multilevel latent class models for cross-classified categorical data: model definition and estimation through stochastic EM. Stat. Comput. 35(2): 50 (2025)- 2024
- [j19]Lucie Derycke
, Stephane Avril, Jeroen K. Vermunt, David Perrin, S. El Batti, Jean-Marc Alsac, J.-N. Albertini, A. Millon:
Computational prediction of proximal sealing in endovascular abdominal aortic aneurysm repair with unfavorable necks. Comput. Methods Programs Biomed. 244: 107993 (2024) - [p2]Emiel Krahmer, Felix J. Clouth, Saar Hommes, Ruben Vromans, Steffen Pauws, Jeroen K. Vermunt, Lonneke V. van de Poll-Franse, Xander Verbeek:
Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options. Commit2Data 2024: 3:1-3:20 - 2022
- [j18]Felix J. Clouth, Steffen Pauws, Floortje Mols, Jeroen K. Vermunt:
A new three-step method for using inverse propensity weighting with latent class analysis. Adv. Data Anal. Classif. 16(2): 351-371 (2022) - [j17]Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo, Jeroen K. Vermunt:
The Spatial Representation of Consumer Dispersion Patterns via a New Multi-level Latent Class Methodology. J. Classif. 39(2): 218-239 (2022) - [j16]Sunghoon Kim, Ashley Stadler Blank, Wayne S. DeSarbo, Jeroen K. Vermunt:
Erratum to: The Spatial Representation of Consumer Dispersion Patterns via a New Multi-level Latent Class Methodology. J. Classif. 39(2): 240 (2022)
2010 – 2019
- 2019
- [j15]Lianne Ippel, Maurits Clemens Kaptein, Jeroen K. Vermunt:
Online estimation of individual-level effects using streaming shrinkage factors. Comput. Stat. Data Anal. 137: 16-32 (2019) - [c7]Saar Hommes, Chris van der Lee, Felix J. Clouth, Jeroen K. Vermunt, Xander Verbeek, Emiel Krahmer:
A Personalized Data-to-Text Support Tool for Cancer Patients. INLG 2019: 443-452 - 2016
- [j14]Margot Bennink, Marcel A. Croon, Brigitte Kroon, Jeroen K. Vermunt:
Micro-macro multilevel latent class models with multiple discrete individual-level variables. Adv. Data Anal. Classif. 10(2): 139-154 (2016) - [j13]Fetene B. Tekle, Dereje W. Gudicha, Jeroen K. Vermunt:
Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models. Adv. Data Anal. Classif. 10(2): 209-224 (2016) - [j12]Dereje W. Gudicha, Fetene B. Tekle, Jeroen K. Vermunt:
Power and Sample Size Computation for Wald Tests in Latent Class Models. J. Classif. 33(1): 30-51 (2016) - [j11]Daniël W. van der Palm, L. Andries Van der Ark, Jeroen K. Vermunt:
Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data. J. Classif. 33(1): 52-72 (2016) - [j10]Lianne Ippel, Maurits Clemens Kaptein, Jeroen K. Vermunt:
Estimating random-intercept models on data streams. Comput. Stat. Data Anal. 104: 169-182 (2016) - 2015
- [j9]José G. Dias, Jeroen K. Vermunt, Sofia Ramos:
Clustering financial time series: New insights from an extended hidden Markov model. Eur. J. Oper. Res. 243(3): 852-864 (2015) - 2014
- [j8]Hans-Hermann Bock, Salvatore Ingrassia, Jeroen K. Vermunt:
Special issue on "Model-based clustering and classification" (part 2). Adv. Data Anal. Classif. 8(1): 1-3 (2014) - 2013
- [j7]Hans-Hermann Bock, Salvatore Ingrassia, Jeroen K. Vermunt:
Special issue on "Model-based clustering and classification". Adv. Data Anal. Classif. 7(3): 237-240 (2013) - [j6]Daniel L. Oberski, Geert H. van Kollenburg, Jeroen K. Vermunt:
A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models. Adv. Data Anal. Classif. 7(3): 267-279 (2013) - [p1]Dereje W. Gudicha, Jeroen K. Vermunt:
Mixture Model Clustering with Covariates Using Adjusted Three-Step Approaches. Algorithms from and for Nature and Life 2013: 87-94 - 2010
- [c6]Daniele Riggi, Jeroen K. Vermunt:
Students Reading Motivation: A Multilevel Mixture Factor Analysis. GfKl 2010: 567-573
2000 – 2009
- 2008
- [j5]José G. Dias, Jeroen K. Vermunt:
A bootstrap-based aggregate classifier for model-based clustering. Comput. Stat. 23(4): 643-659 (2008) - [c5]Olga Lukociene, Jeroen K. Vermunt:
Determining the Number of Components in Mixture Models for Hierarchical Data. GfKl 2008: 241-249 - [c4]José G. Dias, Jeroen K. Vermunt, Sofia Ramos:
Mixture Hidden Markov Models in Finance Research. GfKl 2008: 451-459 - 2007
- [j4]Joost R. Van Ginkel, L. Andries Van der Ark, Klaas Sijtsma, Jeroen K. Vermunt:
Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation. Comput. Stat. Data Anal. 51(8): 4013-4027 (2007) - [j3]Jeroen K. Vermunt:
A hierarchical mixture model for clustering three-way data sets. Comput. Stat. Data Anal. 51(11): 5368-5376 (2007) - [j2]Matthijs Kalmijn, Jeroen K. Vermunt:
Homogeneity of social networks by age and marital status: A multilevel analysis of ego-centered networks. Soc. Networks 29(1): 25-43 (2007) - 2004
- [c3]Jay Magidson, Jeroen K. Vermunt:
An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables. GfKl 2004: 176-183 - [c2]Jeroen K. Vermunt, Jay Magidson:
Hierarchical Mixture Models for Nested Data Structures. GfKl 2004: 240-247 - 2003
- [j1]Jeroen K. Vermunt, Jay Magidson:
Latent class models for classification. Comput. Stat. Data Anal. 41(3-4): 531-537 (2003) - [c1]Jeroen K. Vermunt:
Applications of Latent Class Analysis in Social Science Research. ECSQARU 2003: 22-36
Coauthor Index
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