Ulf Schepsmeier
- Finance top 2%
- Financial Risk and Volatility Modeling 10
- Statistics and Probability top 2%
- Statistical Methods and Inference 3
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- Monetary Policy and Economic Impact 6
- Economics and Econometrics top 5%
- Market Dynamics and Volatility 3
- Spatial and Panel Data Analysis 2
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- Data Analysis with R 2
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- Soil Geostatistics and Mapping 2
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- Structural Health Monitoring Techniques 1
Ulf Schepsmeier
17 papers receiving 789 citations
Hit Papers
Peers
Comparison fields: 5 of 93
- Finance 273
- Statistics and Probability 147
- General Economics, Econometrics and Finance 91
- Statistics, Probability and Uncertainty 75
- Economics and Econometrics 264
Countries citing papers authored by Ulf Schepsmeier
This map shows the geographic impact of Ulf Schepsmeier'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 Ulf Schepsmeier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ulf Schepsmeier more than expected).
Fields of papers citing papers by Ulf Schepsmeier
This network shows the impact of papers produced by Ulf Schepsmeier. 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 Ulf Schepsmeier. The network helps show where Ulf Schepsmeier may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Ulf Schepsmeier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Statistical Inference of Vine Copulas [R package VineCopula version 2.4.1] | 2020 | 19 |
| 2 | 2019 | 6 | |
| 3 | 2016 | 22 | |
| 4 | 2015 | 24 | |
| 5 | 2015 | 53 | |
| 6 | 2015 | 52 | |
| 7 | 2015 | 10 | |
| 8 | Estimating standard errors and efficient goodness-of-fit tests for regular vine copula models | 2014 | 3 |
| 9 | Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine | 2013 | 25 |
| 10 | CDVine: Modeling Dependence with C- and D-Vine Copulas in R | 2013 | 23 |
| 11 | 2013 | 16 | |
| 12 | 2013 | 42 | |
| 13 | Modeling Dependence with C- and D-Vine Copulas: TheRPackageCDVinebreakdown → | 2013 | 328 |
| 14 | 2013 | 43 | |
| 15 | Web supplement: Derivatives and Fisher information of bivariate copulas | 2012 | 4 |
| 16 | 2012 | 145 | |
| 17 | Maximum likelihood estimation of C-vine pair-copula constructions based on bivariate copulas from different families | 2010 | 10 |
About Ulf Schepsmeier
Ulf Schepsmeier is a scholar working on Finance, General Economics, Econometrics and Finance, Statistics and Probability, Economics and Econometrics and Environmental Engineering, having authored 17 papers that have together received 825 indexed citations. Recurring topics across this work include Financial Risk and Volatility Modeling (10 papers), Monetary Policy and Economic Impact (6 papers), Market Dynamics and Volatility (3 papers), Statistical Methods and Inference (3 papers), Data Analysis with R (2 papers), Spatial and Panel Data Analysis (2 papers), Soil Geostatistics and Mapping (2 papers) and Structural Health Monitoring Techniques (1 paper). The work is most often cited by research in Finance (273 citations), Statistics and Probability (147 citations), General Economics, Econometrics and Finance (91 citations), Statistics, Probability and Uncertainty (75 citations) and Economics and Econometrics (264 citations). Ulf Schepsmeier has collaborated with scholars based in Germany, Australia and Canada. Frequent co-authors include Eike Christian Brechmann, Claudia Czado, Aleksey Min, Jakob Stöber, Tobias Erhardt, Eike Brechmann, Benjamin Peherstorfer, Jochen Garcke, Bastian Bohn and Thomas Nagler. Their work appears in journals such as Journal of Multivariate Analysis, Journal of Statistical Software, Econometric Reviews, Journal of the Royal Statistical Society Series C (Applied Statistics) and Statistical Modelling.
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.