17 citations · 27 across the 14 of their papers we have counts for
3 papers · 1 filter
Machine learning in an expectation-maximisation framework for nowcasting
Paul Wilsens, Katrien Antonio, Gerda Claeskens
Decision making often occurs in the presence of incomplete information, leading to the under- or overestimation of risk. Leveraging the observable information to learn the complete…
On dimension reduction in conditional dependence models
Thomas Nagler, Gerda Claeskens, Irène Gijbels
Inference of the conditional dependence structure is challenging when many covariates are present. In numerous applications, only a low-dimensional projection of the covariates inf…
Selective Inference in Graphical Models via Maximum Likelihood
Sofia Guglielmini, Gerda Claeskens, Snigdha Panigrahi
The graphical lasso is a widely used algorithm for fitting undirected Gaussian graphical models. However, for inference on functionals of edge values in the learned graph, standard…