Ludger Evers

420 citations
20 papers · 299 · h-index 10

Impact in

Papers in

Ludger Evers

17 papers receiving 275 citations

Peers

Ludger Evers
Comparison fields: 5 of 94
  • Statistics and Probability 50
  • Computational Mathematics 3
  • Geochemistry and Petrology 20
  • Environmental Engineering 48
  • Computer Vision and Pattern Recognition 51
Replace Ernst Stadlober with:
Ernst Stadlober Austria
Shiraj Khan United States
Zheming Yuan China
Gabriele Lombardi Italy
A. D. Lunn United Kingdom
Mirkamal Mirnia Iran
Asad Ali Shah United States
Jakob W. Messner Austria
K. L. Majumder India
Ludger Evers relative to Ernst Stadlober Austria Ernst Stadlober's profile →
Citations per field
00.5×6.7×
Ernst Stadlober · 1×
Citations per year

Countries citing papers authored by Ludger Evers

Since Specialization
Citations

This map shows the geographic impact of Ludger Evers'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 Ludger Evers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ludger Evers more than expected).

Fields of papers citing papers by Ludger Evers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ludger Evers. 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 Ludger Evers. The network helps show where Ludger Evers may publish in the future.

Co-authors

The 20 scholars most cited alongside Ludger Evers, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ludger Evers Line = papers co-authored together Ludger Evers links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 200864
2 200564
3 201833
4 201428
5 201724
6 201014
7 200712
8 200910
9 200910
10 20079
11 20058
12 20157
13
A dynamic acoustic view of real-time change in word-final liquids in spontaneous Glaswegian
20156
14
Localized regression on principal manifolds.
20104
15 20142
16 20172
17 20071
18
MSc in Bioinformatics: Statistical Data Mining
20041
19
Empirical Bayes Thresholding and Related Methods [R package EbayesThresh version 1.4-12]
20170
20 20210

About Ludger Evers

Ludger Evers is a scholar working on Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Signal Processing and Applied Mathematics, having authored 20 papers that have together received 299 indexed citations. Recurring topics across this work include Statistical Methods and Inference (6 papers), Statistical Methods and Bayesian Inference (3 papers), Soil Geostatistics and Mapping (3 papers), Statistical and numerical algorithms (3 papers), Bayesian Methods and Mixture Models (3 papers), Advanced Clustering Algorithms Research (2 papers), Blind Source Separation Techniques (2 papers) and Spectroscopy and Chemometric Analyses (2 papers). The work is most often cited by research in Statistics and Probability (50 citations), Computational Mathematics (3 citations), Geochemistry and Petrology (20 citations), Environmental Engineering (48 citations) and Computer Vision and Pattern Recognition (51 citations). Ludger Evers has collaborated with scholars based in United Kingdom, Germany and Netherlands. Frequent co-authors include Jochen Einbeck, Claudia‐Martina Messow, Gerhard Tutz, Adrian Bowman, Wayne Jones, Agostino Nobile, Michael Spence, Tereza Neocleous, Matthijs Bonte and Timothy Heaton. Their work appears in journals such as Statistics and Computing, Bioinformatics, Lecture notes in computational science and engineering, Environmetrics and Environmental Modelling & Software.

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.

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