1 citations · 2 across the 4 of their papers we have counts for
5 papers
Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data
Marina T. Dietrich, Dennis Dobler, Mathisca C. M. de Gunst
Fine-Gray models specify the subdistribution hazards for one out of multiple competing risks to be proportional. The estimators of parameters and cumulative incidence functions und…
Wild Bootstrap for Counting Process-Based Statistics
Marina T. Dietrich, Dennis Dobler, Mathisca C. M. de Gunst
The wild bootstrap is a popular resampling method in the context of time-to-event data analyses. Previous works established the large sample properties of it for applications to di…
Gene network reconstruction using global-local shrinkage priors
Gwenaël G. R. Leday, Mathisca C. M. de Gunst, Gino B. Kpogbezan +3
Reconstructing a gene network from high-throughput molecular data is often a challenging task, as the number of parameters to estimate easily is much larger than the sample size. A…
A three domain covariance framework for EEG/MEG data
Beata Roś, Fetsje Bijma, Mathisca de Gunst +1
In this paper we introduce a covariance framework for the analysis of EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-tria…
Existence and uniqueness of the maximum likelihood estimator for models with a Kronecker product covariance structure
Beata Roś, Fetsje Bijma, Jan C. de Munck +1
This paper deals with multivariate Gaussian models for which the covariance matrix is a Kronecker product of two matrices. We consider maximum likelihood estimation of the model pa…