Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Van der Vaart, Aad W."'
In causal inference, sensitivity analysis is important to assess the robustness of study conclusions to key assumptions. We perform sensitivity analysis of the assumption that missing outcomes are missing completely at random. We follow a Bayesian ap
Externí odkaz:
http://arxiv.org/abs/2305.06816
Publikováno v:
Biometrical Journal, 64(7): 1289-1306 (2022)
The features in high dimensional biomedical prediction problems are often well described with lower dimensional manifolds. An example is genes that are organised in smaller functional networks. The outcome can then be described with the factor regres
Externí odkaz:
http://arxiv.org/abs/2104.02419
We introduce a sparse high-dimensional regression approach that can incorporate prior information on the regression parameters and can borrow information across a set of similar datasets. Prior information may for instance come from previous studies
Externí odkaz:
http://arxiv.org/abs/1901.10217
Publikováno v:
Biostatistics, 22(4): 723-737, 2021
In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (a) p-values from a previous study, (b) a summary of prior information, and (c) omics annotatio
Externí odkaz:
http://arxiv.org/abs/1805.00389
For a general class of priors based on random series basis expansion, we develop the Bayes Lepski's method to estimate unknown regression function. In this approach, the series truncation point is determined based on a stopping rule that balances the
Externí odkaz:
http://arxiv.org/abs/1711.06926
Publikováno v:
The Annals of Statistics, 2020 Oct 01. 48(5), 2848-2878.
Externí odkaz:
https://www.jstor.org/stable/27028724
Autor:
Kpogbezan, Gino B., van der Vaart, Aad W., van Wieringen, Wessel N., Leday, Gwenaël G. R., van de Wiel, Mark A.
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, o
Externí odkaz:
http://arxiv.org/abs/1605.07514
Publikováno v:
In Journal of Pharmaceutical Sciences April 2021 110(4):1643-1651
Autor:
Leday, Gwenaël G. R., de Gunst, Mathisca C. M., Kpogbezan, Gino B., Van der Vaart, Aad W., Van Wieringen, Wessel N., Van de Wiel, Mark A.
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 conventional remedy is to regularize or penalize the model likelihood
Externí odkaz:
http://arxiv.org/abs/1510.03771
High dimensional statistics deals with the challenge of extracting structured information from complex model settings. Compared with the growing number of frequentist methodologies, there are rather few theoretically optimal Bayes methods that can de
Externí odkaz:
http://arxiv.org/abs/1506.02174