Ensemble learning combines many algorithms or models to obtain better predictive performance. Ensembles have produced the winning algorithm in competitions such as Netflix Prize. They are used climate modeling and relied upon make daily forecasts. Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods presents challenges because class labels ground truth is ...

Dichotomous IRT models can be viewed as families of stochastically ordered distributions of responses to test items. This paper explores several properties of such distributions. More in particular, it is examined under what conditions stochastic order in families of conditional distributions is transferred to their inverse distributions, from two families of related distributions to a third fa...

Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed within educational research, they are now also being used when focus is on e.g. physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal mode...

Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals’ outcomes differ substantially when ta...