17 citations · 27 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Reducing the dimensionality and granularity in hierarchical categorical variables
Paul Wilsens, Katrien Antonio, Gerda Claeskens
Hierarchical categorical variables often exhibit many levels (high granularity) and many classes within each level (high dimensionality). This may cause overfitting and estimation…
Selective inference using randomized group lasso estimators for general models
Yiling Huang, Sarah Pirenne, Snigdha Panigrahi +1
Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential famil…