7 papers
Focused Information Criteria
Gerda Claeskens, Nils Lid Hjort
The focused information criterion is used to make a choice among several statistical models, or among several variables to include in a model. Different from other such information…
Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution
Gerda Claeskens, Céline Cunen, Nils Lid Hjort
Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model…
Rejoinder to the discussants of the two JASA articles `Frequentist Model Averaging' and `The Focused Information Criterion', by Nils Lid Hjort and Gerda Claeskens
Nils Lid Hjort, Gerda Claeskens
We are honoured to have our work read and discussed at such a thorough level by several experts. Words of appreciation and encouragement are gratefully received, while the many sup…
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…