Profile

Professional career

• 10/2018 - 01/2020
  Academic Employee at University of Mannheim
• 02/2020 - 06/2021
  Academic Employee at KU Leuven
• 07/2021 - 03/2022
  Research Officer at The London School of Economics and Political Science (LSE)
• since 04/2022
  Juniorprofessor for Statistics at Leipzig University

Education

• 10/2015 - 04/2019
  PhD at Heidelberg University

In general my research interests are mostly centred around the analysis of Statistical Models for Network Data, particularly, Semi- and Non-parametric Methods, Dependence, Relational Event Models using counting processes, and Bootstrap methods. In addition, I'm interested in Causal Inference, High-Dimensional Statistics, Quantile Regression, Measurement Error Problems, Hawkes Processes and Survival Analysis. It is particularly interesting if some of these areas overlap.

• Rothe, C.; Kreiß, A.

  Inference in Regression Discontinuity Designs with High-Dimensional Covariates

  The Econometrics Journal. 2022.
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• Mammen, E.; Polonik, W.; Kreiß, A.

  Nonparametric inference for continuous-time event counting and link-based dynamic network models

  Electronic Journal of Statistics. 2019. 13 (2). pp. 2764–2829.
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• Kreiß, A.

  Correlation bounds, mixing and m-dependence under random time-varying network distances with an application to Cox-Processes

  Bernoulli. 2021. 27 (3). pp. 1666–1694.
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• Kreiß, A.; Van Keilegom, I.

  Semi-Parametric Estimation of Incubation and Generation Times by Means of Laguerre Polynomials

  Journal of Nonparametric Statistics. 2022. 34 (3). pp. 570–606.
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• Mammen, E.; Polonik, W.; Kreiß, A.

  Testing For Global Covariate Effects in Dynamic Interaction Event Networks

  Journal of Business & Economic Statistics. 2023.

  DOI: 10.1080/07350015.2023.2263537
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more publications

• Statistical Network Analysis

  Aim of this course is to discuss problems relating to statistical network analysis. Networked data can for example appear in the anaylsis of social networks or economic applications. Possible topics are: Descriptive statistics of networks, network-sampling, models for network data, clustering. The concenpts of the lectures will be illustrated through examples.

  The first half of the course is a lecture-class, the second half of the course is a seminar.

• Mathematical Statistics

  In the first part of the course we want to understand the general, asymptotic theory of estimators in parametric models: We consider maximum likelihood estimation and show that it is optimal in a certain sense.

  In the second part we study linear models and their extensions (generalised linear models and so-called random effects). Those play an important role in many applications.

  In the last part of the course we discuss non-parametric kernel estimators.

• Causal Inference in Statistics

  It is often of interest to understand if one variable is causal for another, e.g., is revising for an exam causal for the grade. The main objective of this course is to understand when estimators allow for this interpretation. To answer these and related questions we will discuss do-calculus and often used concepts like the potential outcomes framework, instrumental variables and differences-in-differences.