Alexander Giessing

Acting Assistant Professor, University of Washington

UW Box Number 354322
Homepage Personal Home Page 
ORCID iD  0000-0002-6917-0652 

Professor Giessing received a B.Sc. in Economics from the University of Bonn, Germany in 2011, a M.Sc. in Econometrics and Mathematical Economics from the London School of Economics and Political Science in 2012, and a Ph.D. in Statistics from the University of Michigan, Ann Arbor in 2018. Before joining the faculty at the University of Washington in 2021, he was Postdoc in the Department of Operations Research and Financial Engineering at Princeton University.

Alexander is broadly interested in inference on high-dimensional data; in particular, in Gaussian and bootstrap approximations and semiparametric efficient inference.


Efficient Inference on High-Dimensional Linear Models with Missing Outcomes
Yikun Zhang, Alexander Giessing, Yen-Chi Chen
This paper is concerned with inference on the regression function of a high-dimensional linear model when outcomes are missing at random. We propose an…

Anti-concentration of Suprema of Gaussian Processes and Gaussian Order Statistics
Alexander Giessing
We derive, up to a constant factor, matching lower and upper bounds on the concentration functions of suprema of separable centered Gaussian processes and…

Gaussian and Bootstrap Approximations for Suprema of Empirical Processes
Alexander Giessing
In this paper we develop non-asymptotic Gaussian approximation results for the sampling distribution of suprema of empirical processes when the indexing…

A Bootstrap Hypothesis Test for High-Dimensional Mean Vectors
Alexander Giessing, Jianqing Fan
This paper is concerned with testing global null hypotheses about population mean vectors of high-dimensional data. Current tests require either strong mixing …

Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores
Alexander Giessing, Jingshen Wang
Understanding treatment effect heterogeneity is vital to many scientific fields because the same treatment may affect different individuals differently…

Bootstrapping $\ell_p$-Statistics in High Dimensions
Alexander Giessing, Jianqing Fan
This paper considers a new bootstrap procedure to estimate the distribution of high-dimensional $\ell_p$-statistics, i.e. the $\ell_p$-norms of the sum of $n$…

On the Predictive Risk in Misspecified Quantile Regression
Alexander Giessing, Xuming He
In the present paper we investigate the predictive risk of possibly misspecified quantile regression functions. The in-sample risk is well-known to be an…

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing
In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component…