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Mathematics
Netherlands
2026

D-Index & Metrics

Mathematics

D-Index
49
Citations
18442
World Ranking
1113
National Ranking
8

Research.com Recognitions

  • 2026 - Research.com Mathematics in Netherlands Leader Award
  • 2025 - Research.com Mathematics in Netherlands Leader Award
  • 2023 - Research.com Mathematics in Netherlands Leader Award

Overview

Aad van der Vaart is affiliated with Delft University of Technology in the Netherlands. Their work spans the fields of Mathematics and Computer Science, with a strong focus on Statistics and Probability and Artificial Intelligence as prominent subfields.

Their research primarily engages with topics including:

  • Statistical Methods and Inference
  • Bayesian Methods and Mixture Models
  • Statistical Methods and Bayesian Inference
  • Gaussian Processes and Bayesian Inference
  • Gene expression and cancer classification
  • Advanced Statistical Methods and Models
  • Numerical methods in inverse problems

Aad van der Vaart has published extensively in various venues. Frequent publication outlets for their work include:

  • arXiv (Cornell University)
  • Electronic Journal of Statistics
  • The Annals of Statistics
  • JAMA Network Open
  • Journal of Pharmaceutical Sciences

Some of their recent published papers are:

  • Stein 1956: Efficient nonparametric testing and estimation (2021), The Annals of Statistics
  • A general framework for Bayes structured linear models (2020), The Annals of Statistics
  • Applying Pattern Recognition as a Robust Approach for Silicone Oil Droplet Identification in Flow-Microscopy Images of Protein Formulations (2020), Journal of Pharmaceutical Sciences
  • Posterolateral or Direct Lateral Surgical Approach for Hemiarthroplasty After a Hip Fracture (2024), JAMA Network Open
  • Semi-supervised empirical Bayes group-regularized factor regression (2022), Biometrical Journal

Frequent collaborators in Aad van der Vaart's body of work include:

  • Jon A. Wellner
  • Botond Szabó
  • Francesco Gili
  • Geurt Jongbloed
  • Stéphanie van der Pas

In addition to research articles, Aad van der Vaart has contributed to academic literature through book publications. Notably, they authored Weak Convergence and Empirical Processes, published by Springer Science+Business Media in 2023.

Best Publications

  • Weak Convergence and Empirical Processes

    Thomas Mikosch;Aad W. Van der Vaart;Jon A. Wellner

  • Convergence rates of posterior distributions

    Subhashis Ghosal;Jayanta K. Ghosh;Aad W. van der Vaart

  • Fundamentals of Nonparametric Bayesian Inference

    Subhashis Ghosal;Aad van der Vaart

  • Uniform Central Limit Theorems

    Unknown

  • BAYESIAN LINEAR REGRESSION WITH SPARSE PRIORS

    Ismaël Castillo;Johannes Schmidt-Hieber;Aad van der Vaart

  • Rates of contraction of posterior distributions based on Gaussian process priors

    van der Aw Aad Vaart;van Jh Harry Zanten

  • Convergence rates of posterior distributions for non-i.i.d. observations

    Subhashis Ghosal;Aad van der Vaart

  • Rates of convergence for Bayes and maximum likelihood estimation for mixture of normal densities

    Subhashis Ghosal;Aad W. van der Vaart

  • Regularization in Statistics

    Peter J. Bickel;Bo Li;Alexandre B. Tsybakov;Sara A. van de Geer

  • On Differentiable Functionals

    Aad Van Der Vaart

  • The Bernstein-Von-Mises theorem under misspecification

    B.J.K. Kleijn;A.W. van der Vaart

  • Needles and Straw in a Haystack: Posterior concentration for possibly sparse sequences

    Ismaël Castillo;Aad van der Vaart

  • Asymptotic Distribution of P Values in Composite Null Models

    James M. Robins;Aad van der Vaart;ValéRie Ventura

  • Posterior convergence rates of Dirichlet mixtures at smooth densities

    Subhashis Ghosal;Aad van der Vaart

  • The horseshoe estimator: Posterior concentration around nearly black vectors

    S.L. van der Pas;B.J.K. Kleijn;A.W. van der Vaart

  • BAYESIAN INVERSE PROBLEMS WITH GAUSSIAN PRIORS

    BT Knapik;van der Aw Aad Vaart;van Jh Harry Zanten

  • Oracle inequalities for multi-fold cross validation

    Aad W. van der Vaart;Sandrine Dudoit;Mark J. van der Laan

  • Higher order influence functions and minimax estimation of nonlinear functionals

    J. Robins;L. Li;E. Tchetgen;A.W. van der Vaart

  • Convergence rates of posterior distributions for noniid observations

    Subhashis Ghosal;Aad van der Vaart

  • The Cross-Validated Adaptive Epsilon-Net Estimator

    Mark J. van der Laan;Sandrine Dudoit;Aad W. van der Vaart

  • Higher order influence functions and minimax estimation of nonlinear functionals

    James Robins;Lingling Li;Eric Tchetgen;Aad van der Vaart

Frequent Co-Authors

Jon A. Wellner
Jon A. Wellner University of Washington
Subhashis Ghosal
Subhashis Ghosal North Carolina State University
James M. Robins
James M. Robins Harvard University
Eric J. Tchetgen Tchetgen
Eric J. Tchetgen Tchetgen University of Pennsylvania
Elena Marchiori
Elena Marchiori Radboud University
Mark J. van der Laan
Mark J. van der Laan University of California, Berkeley
Bauke Ylstra
Bauke Ylstra Vrije Universiteit Amsterdam
Dorret I. Boomsma
Dorret I. Boomsma Vrije Universiteit Amsterdam
Eus J.W. Van Someren
Eus J.W. Van Someren Vrije Universiteit Amsterdam
Bin Yu
Bin Yu University of California, Berkeley

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