Anton Rask Lundborg

Office 04.3.01

Department of Mathematical Sciences

University of Copenhagen

Universitetsparken 5

2100 København Ø

Denmark

I am a postdoc at the Copenhagen Causality Lab, University of Copenhagen. My current research focuses on non- and semiparametric inference for a variety of different data structures and problems.

My research interests include developing new methods with theoretical guarantees and analysing current methods in the fields of causal inference, semi- and nonparametric statistics and non-euclidean statistics.

I am also interested in more foundational and philosophical problems in statistics and causal inference in particular.

I am supervised by Niklas Pfister. Previously, I did a PhD under the supervision of Rajen Shah and Richard Samworth at the Statistical Laboratory, University of Cambridge and a master’s thesis under the supervision of Jonas Peters at the University of Copenhagen.

Keywords: statistics, theoretical statistics, conditional independence testing, causal inference, semiparametric statistics, nonparametric statistics, non-euclidean statistics

Selected papers

arXiv

The Projected Covariance Measure for assumption-lean variable significance testing

Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah, and Richard J. Samworth

2022

arXiv Bib

@misc{lundborg2022projected,
  doi = {10.48550/ARXIV.2211.02039},
  author = {Lundborg, Anton Rask and Kim, Ilmun and Shah, Rajen D. and Samworth, Richard J.},
  keywords = {Statistics Theory (math.ST), Methodology (stat.ME), Machine Learning (stat.ML), FOS: Mathematics, FOS: Mathematics, FOS: Computer and information sciences, FOS: Computer and information sciences, 62G10},
  title = {The Projected Covariance Measure for assumption-lean variable significance testing},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International},
}

arXiv

Confounder Selection: Objectives and Approaches

F. Richard Guo, Anton Rask Lundborg, and Qingyuan Zhao

2022

arXiv Bib

@misc{guo2022confounder,
  doi = {10.48550/ARXIV.2208.13871},
  author = {Guo, F. Richard and Lundborg, Anton Rask and Zhao, Qingyuan},
  keywords = {Methodology (stat.ME), Statistics Theory (math.ST), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
  title = {Confounder Selection: Objectives and Approaches},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International},
}

JRSSB

Conditional independence testing in Hilbert spaces with applications to functional data analysis

Anton Rask Lundborg, Rajen D. Shah, and Jonas Peters

Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2022

PDF Bib

@article{lundborg2022conditional,
  author = {Lundborg, Anton Rask and Shah, Rajen D. and Peters, Jonas},
  title = {Conditional independence testing in Hilbert spaces with applications to functional data analysis},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume = {84},
  number = {5},
  pages = {1821-1850},
  keywords = {functional graphical model, function-on-function regression, significance testing, truncated functional linear model, uniform type I error control},
  doi = {https://doi.org/10.1111/rssb.12544},
  year = {2022},
}

News

Nov 2022	Paper on conditional independence testing for functional data is published in the Journal of The Royal Statistical Society: Series B.
Nov 2022	New paper on arXiv: The Projected Covariance Measure for assumption-lean variable significance testing
Oct 2022	Started postdoc at the University of Copenhagen supervised by Niklas Pfister.
Aug 2022	New paper on arXiv: Confounder Selection: Objectives and Approaches.
Jan 2021	First paper is on arXiv: Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data.
Dec 2020	Gave a talk at CMStatistics 2020 about upcoming work on conditional independence testing for functional data.
Jan 2020	Started position as a PhD Student at the Unversity of Cambridge supervised by Rajen Shah and Richard Samworth.