38 citations · 139 across the 9 of their papers we have counts for
9 papers
Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Marjolein Fokkema, Achim Zeileis
Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and re…
Imputation of missing values in multi-view data
Wouter van Loon, Marjolein Fokkema, Frank de Vos +3
Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all feature…
Improved prediction rule ensembling through model-based data generation
Benny Markovitch, Marjolein Fokkema
Prediction rule ensembles (PRE) provide interpretable prediction models with relatively high accuracy.PRE obtain a large set of decision rules from a (boosted) decision tree ensemb…
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
Wouter van Loon, Frank de Vos, Marjolein Fokkema +4
Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (S…
View selection in multi-view stacking: Choosing the meta-learner
Wouter van Loon, Marjolein Fokkema, Botond Szabo +1
Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learne…
The Early Roots of Statistical Learning in the Psychometric Literature: A review and two new results
Mark de Rooij, Bunga Citra Pratiwi, Marjolein Fokkema +2
Machine and Statistical learning techniques become more and more important for the analysis of psychological data. Four core concepts of machine learning are the bias variance trad…
Fitting Prediction Rule Ensembles to Psychological Research Data: An Introduction and Tutorial
Marjolein Fokkema, Carolin Strobl
Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive accuracy and interpretability. Starting from a d…
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
Wouter van Loon, Marjolein Fokkema, Botond Szabo +1
In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to t…
Fitting Prediction Rule Ensembles with R Package pre
Marjolein Fokkema
Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models. This paper presents the R package pre, which d…