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Mathematics

D-Index
38
Citations
6008
World Ranking
2345
National Ranking
144

Research.com Recognitions

  • 2009 - Fellow of Alfred P. Sloan Foundation

Overview

Mathias Drton is affiliated with the Technical University of Munich in Germany. Their research spans multiple fields, focusing mainly on Computer Science and Mathematics.

Their primary areas of study include:

  • Artificial Intelligence
  • Statistics and Probability
  • Molecular Biology
  • Computational Theory and Mathematics
  • Management Science and Operations Research

Drton's main topics of work cover:

  • Bayesian Modeling and Causal Inference
  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Bayesian Methods and Mixture Models
  • Advanced Statistical Methods and Models
  • Gene Regulatory Network Analysis
  • Advanced Causal Inference Techniques

They have published extensively, with frequent publication venues including:

  • arXiv (Cornell University)
  • The Annals of Statistics
  • Bernoulli
  • PLoS Genetics
  • Biometrika

Among their recent papers are:

  • "Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila," 2020, PLoS Genetics
  • "Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs," 2020, Journal of the American Statistical Association
  • "On the power of Chatterjee's rank correlation," 2021, Biometrika
  • "High-dimensional consistent independence testing with maxima of rank correlations," 2020, The Annals of Statistics
  • "On universally consistent and fully distribution-free rank tests of vector independence," 2022, The Annals of Statistics

Frequent co-authors who have collaborated closely with Drton include:

  • Hongjian Shi
  • Ali Shojaie
  • David Strieder
  • Nils Sturma
  • Shiqing Yu

Drton has received recognition such as the Fellow of Alfred P. Sloan Foundation award in 2009.

Best Publications

  • Extended Bayesian Information Criteria for Gaussian Graphical Models

    Rina Foygel;Mathias Drton

  • Lectures on Algebraic Statistics

    Mathias Drton;Bernd Sturmfels;Seth Sullivant

  • Model selection for Gaussian concentration graphs

    Mathias Drton;Michael D. Perlman

  • Structure Learning in Graphical Modeling

    Mathias Drton;Marloes H. Maathuis

  • Handbook of Graphical Models

    Marloes H. Maathuis;Mathias Drton;Steffen Lauritzen;Martin Wainwright

  • Estimation of a covariance matrix with zeros

    Sanjay Chaudhuri;Mathias Drton;Thomas S. Richardson

  • Multiple Testing and Error Control in Gaussian Graphical Model Selection

    Mathias Drton;Michael D. Perlman

  • Robust graphical modeling of gene networks using classical and alternative t-distributions

    Michael Finegold;Mathias Drton

  • Discrete chain graph models

    Mathias Drton

  • A SINful approach to Gaussian graphical model selection

    Mathias Drton;Michael D. Perlman

  • High-dimensional Ising model selection with Bayesian information criteria

    Rina Foygel Barber;Mathias Drton

  • A Bayesian information criterion for singular models

    Mathias Drton;Martyn Plummer

  • PC algorithm for nonparanormal graphical models

    Naftali Harris;Mathias Drton

  • Likelihood ratio tests and singularities

    Mathias Drton

  • Seat biases of apportionment methods for proportional representation

    Karsten Schuster;Friedrich Pukelsheim;Mathias Drton;Norman R. Draper

  • Algebraic factor analysis: tetrads, pentads and beyond

    Mathias Drton;Bernd Sturmfels;Seth Sullivant

  • Global identifiability of linear structural equation models

    Mathias Drton;Rina Foygel;Seth Sullivant

  • Binary models for marginal independence

    Mathias Drton;Thomas S. Richardson

  • Estimation of High-Dimensional Graphical Models Using Regularized Score Matching.

    Lina Lin;Mathias Drton;Ali Shojaie

  • Half-trek criterion for generic identifiability of linear structural equation models

    Rina Foygel;Jan Draisma;Mathias Drton

  • Multimodality of the likelihood in the bivariate seemingly unrelated regressions model

    Mathias Drton;Thomas S. Richardson

  • Algebraic Factor Analysis: Tetrads, Pentads and Beyond

    Mathias Drton;Bernd Sturmfels;Seth Sullivant

Frequent Co-Authors

Thomas S. Richardson
Thomas S. Richardson University of Washington
Seth Sullivant
Seth Sullivant North Carolina State University
Michael D. Perlman
Michael D. Perlman University of Washington
Bernd Sturmfels
Bernd Sturmfels Max Planck Institute for Mathematics in the Sciences
Marc Hallin
Marc Hallin Université Libre de Bruxelles
John Lafferty
John Lafferty Yale University
Raphael Gottardo
Raphael Gottardo Fred Hutchinson Cancer Research Center
Daniel E. L. Promislow
Daniel E. L. Promislow University of Washington

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