Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Conditional variable importance for random forests
Countries citing papers authored by Thomas Augustin
Since
Specialization
Citations
This map shows the geographic impact of Thomas Augustin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas Augustin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Augustin more than expected).
This network shows the impact of papers produced by Thomas Augustin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas Augustin. The network helps show where Thomas Augustin may publish in the future.
Co-authorship network of co-authors of Thomas Augustin
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Augustin.
A scholar is included among the top collaborators of Thomas Augustin based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Thomas Augustin. Thomas Augustin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Augustin, Thomas, et al.. (2021). Towards Improving Electoral Forecasting by Including Undecided Voters and Interval-valued Prior Knowledge.. 201–209.
Augustin, Thomas, et al.. (2019). Constructing Simulation Data with Dependency Structure for Unreliable Single-Cell RNA-Sequencing Data Using Copulas. 216–224.1 indexed citations
5.
Augustin, Thomas, et al.. (2019). Imprecise Hypothesis-Based Bayesian Decision Making with Simple Hypotheses.. 338–345.1 indexed citations
6.
Augustin, Thomas, et al.. (2017). Towards a Cautious Modelling of Missing Data in Small Area Estimation. 253–264.2 indexed citations
7.
Augustin, Thomas & Rudolf Seising. (2017). Kurt Weichselberger's Contribution to Imprecise Probabilities. 13–24.2 indexed citations
8.
Augustin, Thomas, et al.. (2016). Statistical Matching of Discrete Data by Bayesian Networks. 159–170.5 indexed citations
Augustin, Thomas, Frank P. A. Coolen, Matthias C. M. Troffaes, & Serafı́n Moral. (2010). Special issue on imprecise probability in statistical inference and decision making. International Journal of Approximate Reasoning.3 indexed citations
Coolen, Frank P. A. & Thomas Augustin. (2005). Learning from multinomial data: a nonparametric predictive alternative to the Imprecise Dirichlet Model. 125–134.21 indexed citations
16.
Utkin, Lev V. & Thomas Augustin. (2005). Powerful algorithms for decision making under partial prior information and general ambiguity attitudes. 349–358.24 indexed citations
Augustin, Thomas. (2003). On the Suboptimality of the Generalized Bayes Rule and Robust Bayesian Procedures from the Decision Theoretic Point of View --- a Cautionary Note on Updating Imprecise Priors.. 31–45.18 indexed citations
19.
Weichselberger, Kurt & Thomas Augustin. (2003). On the Symbiosis of Two Concepts of Conditional Interval Probability.. 59(3). 606–9.17 indexed citations
20.
Augustin, Thomas. (2001). On Decision Making under Ambiguous Prior and Sampling Information.. 9–16.18 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.