What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Jeroen K. Vermunt spends much of his time researching Latent class model, Latent variable model, Statistics, Artificial intelligence and Econometrics.
The various areas that Jeroen K. Vermunt examines in his Latent class model study include Latent variable, Cluster analysis, Categorical variable, Applied mathematics and Multilevel model.
His research integrates issues of Probabilistic latent semantic analysis, Parametric statistics, Algebra and Expectation–maximization algorithm in his study of Latent variable model.
His work on Covariate and Nonparametric regression as part of general Statistics research is frequently linked to Class and Longevity risk, thereby connecting diverse disciplines of science.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Pattern recognition and Natural language processing.
His Econometrics study integrates concerns from other disciplines, such as Juvenile delinquency, Recidivism and Markov chain, Markov model.
His most cited work include:
- Latent Class Cluster Analysis (1096 citations)
- Latent Class Modeling with Covariates: Two Improved Three-Step Approaches (875 citations)
- A Poisson log-bilinear regression approach to the construction of projected lifetables (507 citations)
What are the main themes of his work throughout his whole career to date?
Jeroen K. Vermunt mainly investigates Latent class model, Statistics, Econometrics, Latent variable model and Categorical variable.
His Latent class model research integrates issues from Mixture model, Probabilistic latent semantic analysis, Latent variable and Artificial intelligence.
His work deals with themes such as Covariance, Multivariate normal distribution, Data mining and Cluster analysis, which intersect with Mixture model.
His work on Log-linear model, Sample size determination, Missing data and Factor analysis is typically connected to Random effects model as part of general Statistics study, connecting several disciplines of science.
Jeroen K. Vermunt studied Econometrics and Markov chain that intersect with Product.
His Latent variable model research is multidisciplinary, relying on both Structural equation modeling, Multilevel model and Applied mathematics.
He most often published in these fields:
- Latent class model (37.82%)
- Statistics (30.53%)
- Econometrics (25.49%)
What were the highlights of his more recent work (between 2015-2021)?
- Latent class model (37.82%)
- Statistics (30.53%)
- Econometrics (25.49%)
In recent papers he was focusing on the following fields of study:
Jeroen K. Vermunt focuses on Latent class model, Statistics, Econometrics, Categorical variable and Latent variable.
Jeroen K. Vermunt performs multidisciplinary studies into Latent class model and Class in his work.
His Econometrics study deals with Goodness of fit intersecting with Type I and type II errors.
His Categorical variable research incorporates elements of Mixture model, Artificial intelligence, Item response theory and Missing data.
His Mixture model research is multidisciplinary, incorporating perspectives in Tree and Data mining.
Jeroen K. Vermunt frequently studies issues relating to Probabilistic latent semantic analysis and Latent variable.
Between 2015 and 2021, his most popular works were:
- Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes (251 citations)
- The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies (139 citations)
- Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (125 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Latent class model, Statistics, Econometrics, Latent variable model and Latent variable.
His Latent class model research is multidisciplinary, incorporating elements of Recidivism, Clinical psychology, Disease, Categorical variable and Cohort.
His study in the fields of Multivariate statistics, Standard error and Item response theory under the domain of Statistics overlaps with other disciplines such as Focus.
His work carried out in the field of Econometrics brings together such families of science as Juvenile delinquency, Software, Local independence and Robustness.
Jeroen K. Vermunt combines subjects such as Sample size determination, Cluster analysis, Probabilistic latent semantic analysis, Markov model and Structural equation modeling with his study of Latent variable model.
His Latent variable study incorporates themes from Covariance, Big Five personality traits, Covariate, Mixture model and Exploratory factor analysis.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
