Jeroen K. Vermunt - researchr alias

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Viewing Publication 1 - 28 from 28

2025

Multilevel latent class models for cross-classified categorical data: model definition and estimation through stochastic EMS. Columbu, N. Piras, Jeroen K. Vermunt. sac, 35(2):50, April 2025. [doi]

2024

2022

2019

Online estimation of individual-level effects using streaming shrinkage factorsLianne Ippel, Maurits Clemens Kaptein, Jeroen K. Vermunt. csda, 137:16-32, 2019. [doi]

2016

Micro-macro multilevel latent class models with multiple discrete individual-level variablesMargot Bennink, Marcel A. Croon, Brigitte Kroon, Jeroen K. Vermunt. adac, 10(2):139-154, 2016. [doi]

Power and Sample Size Computation for Wald Tests in Latent Class ModelsDereje W. Gudicha, Fetene B. Tekle, Jeroen K. Vermunt. classification, 33(1):30-51, 2016. [doi]

Estimating random-intercept models on data streamsL. Ippel, Maurits Clemens Kaptein, Jeroen K. Vermunt. csda, 104:169-182, 2016. [doi]

Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class modelsFetene B. Tekle, Dereje W. Gudicha, Jeroen K. Vermunt. adac, 10(2):209-224, 2016. [doi]

Divisive Latent Class Modeling as a Density Estimation Method for Categorical DataDaniël W. van der Palm, L. Andries Van der Ark, Jeroen K. Vermunt. classification, 33(1):52-72, 2016. [doi]

2015

Clustering financial time series: New insights from an extended hidden Markov modelJosé G. Dias, Jeroen K. Vermunt, Sofia Ramos. eor, 243(3):852-864, 2015. [doi]

2014

Special issue on "Model-based clustering and classification" (part 2)Hans-Hermann Bock, Salvatore Ingrassia, Jeroen K. Vermunt. adac, 8(1):1-3, 2014. [doi]

2013

Special issue on "Model-based clustering and classification"Hans-Hermann Bock, Salvatore Ingrassia, Jeroen K. Vermunt. adac, 7(3):237-240, 2013. [doi]

Mixture Model Clustering with Covariates Using Adjusted Three-Step ApproachesDereje W. Gudicha, Jeroen K. Vermunt. In Berthold Lausen, Dirk Van den Poel, Alfred Ultsch, editors, Algorithms from and for Nature and Life - Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, pages 87-94, Springer, 2013. [doi]

A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class modelsDaniel L. Oberski, Geert H. van Kollenburg, Jeroen K. Vermunt. adac, 7(3):267-279, 2013. [doi]

2010

Students Reading Motivation: A Multilevel Mixture Factor AnalysisDaniele Riggi, Jeroen K. Vermunt. gfkl 2012: 567-573 [doi]

2008

A bootstrap-based aggregate classifier for model-based clusteringJosé G. Dias, Jeroen K. Vermunt. cstat, 23(4):643-659, 2008. [doi]

Mixture Hidden Markov Models in Finance ResearchJosé G. Dias, Jeroen K. Vermunt, Sofia Ramos. gfkl 2010: 451-459 [doi]

Determining the Number of Components in Mixture Models for Hierarchical DataOlga Lukociene, Jeroen K. Vermunt. gfkl 2010: 241-249 [doi]

2007

Two-way imputation: A Bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximationJoost R. Van Ginkel, L. Andries Van der Ark, Klaas Sijtsma, Jeroen K. Vermunt. csda, 51(8):4013-4027, 2007. [doi]

Homogeneity of social networks by age and marital status: A multilevel analysis of ego-centered networksMatthijs Kalmijn, Jeroen K. Vermunt. socnet, 29(1):25-43, 2007. [doi]

A hierarchical mixture model for clustering three-way data setsJeroen K. Vermunt. csda, 51(11):5368-5376, 2007. [doi]

2004

An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent VariablesJay Magidson, Jeroen K. Vermunt. gfkl 2005: 176-183 [doi]

Hierarchical Mixture Models for Nested Data StructuresJeroen K. Vermunt, Jay Magidson. gfkl 2005: 240-247 [doi]

2003

Applications of Latent Class Analysis in Social Science ResearchJeroen K. Vermunt. ecsqaru 2003: 22-36 [doi]

Latent class models for classificationJeroen K. Vermunt, Jay Magidson. csda, 41(3-4):531-537, 2003. [doi]

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