3 citations · 4 across the 5 of their papers we have counts for
7 papers
Deducing neighborhoods of classes from a fitted model
Alexander Gerharz, Andreas Groll, Gunther Schauberger
In todays world the request for very complex models for huge data sets is rising steadily. The problem with these models is that by raising the complexity of the models, it gets mu…
Random boosting and random^2 forests -- A random tree depth injection approach
Tobias Markus Krabel, Thi Ngoc Tien Tran, Andreas Groll +2
The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel r…
A flexible adaptive lasso Cox frailty model based on the full likelihood
Maike Hohberg, Andreas Groll
In this work a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full instead of the p…
Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques
Colin Griesbach, Andreas Groll, Elisabeth Waldmann
Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can rough…
Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches
Hendrik van der Wurp, Andreas Groll, Thomas Kneib +2
We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear depende…
Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019
Andreas Groll, Christophe Ley, Gunther Schauberger +2
In this work, we combine two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking…