M. van Smeden
Statistics and Probability · 2018–2025
A researcher in the Science corpus: 7 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 81 citations into it
- Artificial Intelligence — 72 citations into it
- Economics and Econometrics — 44 citations into it
Whom M. van Smeden trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ewout W. Steyerberg
- Eric R. Ziegel
- Karel G. M. Moons
- Sunil Rao
- Jack P. C. Kleijnen
- M. Pavlou
- Douglas G. Altman
- Andrew Gelman
- Georg Heinze
- Gary S. Collins
Papers from M. van Smeden's perspective
Ordered by how strongly the researchers M. van Smeden trusts endorse each paper. Same corpus, a different order for every researcher.
- A solution to the problem of separation in logistic regression (Statistics in Medicine)
- A weakly informative default prior distribution for logistic and other regression models
- Estimation with Quadratic Loss
- Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- A methodology for fitting and validating metamodels in simulation (European Journal of Operational Research)
- Key challenges for delivering clinical impact with artificial intelligence
- Stein's Paradox in Statistics
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Their papers
- Calculating the sample size required for developing a clinical prediction model (2020) — 2,194 citations
- Sample size for binary logistic prediction models: Beyond events per variable criteria (2018) — 679 citations
- Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence (2021) — 676 citations
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) — 2,500 citations
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods (2025) — 435 citations
- Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) — 377 citations
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal (2020) — 2,435 citations