I’m an assistant professor in Statistics at the LUMC and the Mathematical Institute of Leiden University, and a guest fellow at the Princess Máxima Center for Pediatric Oncology.
Sparsity; Causality; Bayesian nonparametrics; Nonparametric regression; Community detection; Survival analysis; Competing risks.
Conference: Survival Analysis for Junior Researchers 2018
SAfJR 2018 will be hosted in Leiden! See the conference website for more information.
12. Posterior Concentration for Bayesian Regression Trees and their Ensembles. [pdf] (with Veronika Rocková)
11. Bayesian Dyadic Trees and Histograms for Regression. [pdf] (with Veronika Rocková, to appear in the NIPS proceedings of 2017)
10. Bayesian Community Detection. [pdf] (with Aad van der Vaart, to appear in Bayesian Analysis)
9. S. van der Pas, R. Nelissen, M. Fiocco (2018). Different competing risks models for different questions may give similar results in arthroplasty registers in the presence of few events. Acta Orthopaedica 89 (2), 145-151. [pdf]
8. S. van der Pas, P. Grünwald (2018). Almost the Best of Three Worlds: Risk, Consistency and Optional Stopping for the Switch Criterion in Nested Model Selection. Statistica Sinica 28, 229-253. [pdf]
7. B.W. Schreurs, S.L. van der Pas (2018). No Benefit of Arthroscopy in Subacromial Shoulder Pain. The Lancet 391 (10118), 289-291. [pdf]
6. S. van der Pas, B. Szabó and A. van der Vaart (2017). Uncertainty Quantification for the Horseshoe (with Discussion). Bayesian Analysis 12 (4), 1221-1274. [pdf]
5. S. van der Pas, B. Szabó and A. van der Vaart (2017). Adaptive Posterior Contraction Rates for the Horseshoe. Electronic Journal of Statistics 11 (2), 3196-3225. [pdf]
4. S.L. van der Pas, R.G.H.H. Nelissen and M. Fiocco (2017). Patients with Staged Bilateral Total Joint Arthroplasty in Registries: Immortal Time Bias and Methodological Options. Journal of Bone & Joint Surgery – American Volume 99 (15), p e82. [pdf] Link to online journal edition here. Copyright JBJS.
3. S.L. van der Pas, J.-B. Salomond and J. Schmidt-Hieber (2016). Conditions for Posterior Contraction in the Sparse Normal Means Problem. Electronic Journal of Statistics 10, 976-1000. [pdf]
2. S.L. van der Pas, B.J.K Kleijn and A.W. van der Vaart (2014). The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors. Electronic Journal of Statistics 8 (2), 2585-2618. [pdf]
1. S. van der Pas (2014). The Normal Road to Geometry: dê in Euclid’s Elements and the Mathematical Competence of His Audience. The Classical Quarterly 64, 558-573. [pdf] Link to online journal edition here. Copyright The Classical Association.
- `Bayesian dyadic trees and histograms for regression’ [poster] (NIPS, Long Beach, December 2017)
- `Posterior concentration for Bayesian regression trees and their ensembles’ [slides] (Workshop on Bayesian and PAC-Bayesian methods, Paris, November 2017)
- `Competing risks with time dependent clustering’ [slides] (PTA, London, September 2016)
- `How many needles in the haystack? Adaptive inference and uncertainty quantification for the horseshoe’ [slides] (JSM, Chicago, August 2016)
- `The horseshoe and more general sparsity priors’ [slides] (EYSM, Prague, September 2015)
- `Bayesian community detection’ [slides] (JSM, Seattle, August 2015)
- `The horseshoe estimator: posterior concentration around nearly black vectors’ [slides] (ERCIM, Pisa, December 2014)
- `Conditions for posterior contraction in the sparse normal means problem’ [slides] (Bayes Club, Amsterdam, April 2016)
- `The horseshoe prior for nearly black vectors’ [slides] (Bayes Club, Amsterdam, April 2014)