6 papers
A Semiparametric Nonlinear Mixed Effects Model with Penalized Splines Using Automatic Differentiation
Matteo D'Alessandro, Magne Thoresen, Ãystein Sørensen
We present an estimation procedure for nonlinear mixed-effects models in which the population trajectory is represented by penalized splines and adapted to individuals via subject-…
False Discovery Rate Control via Bayesian Mirror Statistic
Marco Molinari, Magne Thoresen
Simultaneously performing variable selection and inference in high-dimensional models is an open challenge in statistics and machine learning. The increasing availability of vast a…
Methods of Selective Inference for Linear Mixed Models: a Review and Empirical Comparison
Matteo D'Alessandro, Magne Thoresen
Selective inference aims at providing valid inference after a data-driven selection of models or hypotheses. It is essential to avoid overconfident results and replicability issues…
Methods for differential network estimation: an empirical comparison
Anna Plaksienko, Magne Thoresen, Vera DjordjiloviÄ
We provide a review and a comparison of methods for differential network estimation in Gaussian graphical models with focus on structure learning. We consider the case of two datas…
Bad estimation, good prediction: the Lasso in dense regimes
Andrea Bratsberg, Magne Thoresen, Jelle J. Goeman
For high-dimensional omics data, sparsity-inducing regularization methods such as the Lasso are widely used and often yield strong predictive performance, even in settings when the…
Conditional variable screening for ultra-high dimensional longitudinal data with time interactions
Andrea Bratsberg, Abhik Ghosh, Magne Thoresen
In recent years we have been able to gather large amounts of genomic data at a fast rate, creating situations where the number of variables greatly exceeds the number of observatio…