Heidi Seibold

735 total citations
21 papers, 293 citations indexed

About

Heidi Seibold is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Heidi Seibold has authored 21 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Statistics and Probability, 6 papers in Artificial Intelligence and 4 papers in Molecular Biology. Recurrent topics in Heidi Seibold's work include Statistical Methods and Inference (3 papers), Data Analysis with R (3 papers) and Statistical Methods and Bayesian Inference (2 papers). Heidi Seibold is often cited by papers focused on Statistical Methods and Inference (3 papers), Data Analysis with R (3 papers) and Statistical Methods and Bayesian Inference (2 papers). Heidi Seibold collaborates with scholars based in Germany, Switzerland and Austria. Heidi Seibold's co-authors include Torsten Hothorn, Achim Zeileis, Anne‐Laure Boulesteix, Sabine Hoffmann, Christoph Bernau, Riccardo De Bin, Björn Bornkamp, Joaquin Vanschoren, Benjamin Hofner and Michel Lang and has published in prestigious journals such as PLoS ONE, Cochrane Database of Systematic Reviews and Journal of Psychiatric Research.

In The Last Decade

Heidi Seibold

16 papers receiving 287 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Heidi Seibold Germany	11	90	62	29	28	20	21	293
Cristina Rueda Spain	12	94 1.0×	49 0.8×	51 1.8×	20 0.7×	16 0.8×	52	405
Nicola Lunardon Italy	5	46 0.5×	98 1.6×	51 1.8×	25 0.9×	17 0.8×	11	409
Ahlem Hajjem Canada	4	57 0.6×	61 1.0×	15 0.5×	14 0.5×	9 0.5×	5	264
Pralay Senchaudhuri United States	16	281 3.1×	63 1.0×	22 0.8×	22 0.8×	22 1.1×	26	547
Özgür Asar Türkiye	10	63 0.7×	29 0.5×	16 0.6×	20 0.7×	8 0.4×	18	327
Vincent Audigier France	6	97 1.1×	59 1.0×	15 0.5×	19 0.7×	11 0.6×	9	324
Yanming Li United States	11	79 0.9×	48 0.8×	76 2.6×	23 0.8×	7 0.3×	45	408
Aaron Fisher United States	7	39 0.4×	101 1.6×	47 1.6×	19 0.7×	5 0.3×	13	357
Robert J. B. Goudie United Kingdom	9	59 0.7×	65 1.0×	9 0.3×	30 1.1×	6 0.3×	28	221
Guido del Pino Chile	10	111 1.2×	28 0.5×	11 0.4×	12 0.4×	9 0.5×	16	370

Countries citing papers authored by Heidi Seibold

Since Specialization

Citations

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

Fields of papers citing papers by Heidi Seibold

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heidi Seibold

This figure shows the co-authorship network connecting the top 25 collaborators of Heidi Seibold. A scholar is included among the top collaborators of Heidi Seibold 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 Heidi Seibold. Heidi Seibold is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Haslinger, Christian, et al.. (2024). What makes forest-based heterogeneous treatment effect estimators work?. The Annals of Applied Statistics. 18(1). 2 indexed citations

Reeve, Kelly, Joachim Havla, Jacob Burns, et al.. (2023). Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis. Cochrane Database of Systematic Reviews. 2023(9). CD013606–CD013606. 15 indexed citations

Saller, Thomas, Heidi Seibold, Baocheng Wang, et al.. (2022). Association between post-operative delirium and use of volatile anesthetics in the elderly: A real-world big data approach. Journal of Clinical Anesthesia. 83. 110957–110957. 13 indexed citations

Steinbach, Peter, et al.. (2021). Teaching Machine Learning in 2020. 1–6.

Seibold, Heidi, et al.. (2021). Statisticians, Roll Up Your Sleeves! There's A Crisis to be Solved. Significance. 18(4). 42–44. 4 indexed citations

Seibold, Heidi, Achim Zeileis, & Torsten Hothorn. (2020). Stratified and Personalised Models Based on Model-Based Trees and Forests [R package model4you version 0.9-6].

Boulesteix, Anne‐Laure, et al.. (2020). A Replication Crisis in Methodological Research?. Significance. 17(5). 18–21. 26 indexed citations

Foster, Simon, et al.. (2019). Estimating patient-specific treatment advantages in the ‘Treatment for Adolescents with Depression Study’. Journal of Psychiatric Research. 112. 61–70. 12 indexed citations

Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, et al.. (2019). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. Zurich Open Repository and Archive (University of Zurich).

10.

Seibold, Heidi, Achim Zeileis, & Torsten Hothorn. (2019). model4you: An R Package for Personalised Treatment Effect Estimation. Journal of Open Research Software. 7(1). 17–17. 12 indexed citations

11.

Lederer, Wolfgang & Heidi Seibold. (2019). SIMEX- And MCSIMEX-Algorithm for Measurement Error Models [R package simex version 1.8]. 1 indexed citations

12.

Bornkamp, Björn, et al.. (2018). Subgroup identification in dose-finding trials via model-based recursive partitioning. Zurich Open Repository and Archive (University of Zurich). 11 indexed citations

13.

Seibold, Heidi, Achim Zeileis, & Torsten Hothorn. (2018). Individual treatment effect prediction for ALS patients. Zurich Open Repository and Archive (University of Zurich).

14.

Seibold, Heidi, Torsten Hothorn, & Achim Zeileis. (2018). Generalised linear model trees with global additive effects. Advances in Data Analysis and Classification. 13(3). 703–725. 21 indexed citations

15.

Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, et al.. (2017). OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics. 34(3). 977–991. 24 indexed citations

16.

Seibold, Heidi, Achim Zeileis, & Torsten Hothorn. (2016). Model-Based Recursive Partitioning for Subgroup Analyses. The International Journal of Biostatistics. 12(1). 45–63. 111 indexed citations

17.

Seibold, Heidi, Christoph Bernau, Anne‐Laure Boulesteix, & Riccardo De Bin. (2016). On the choice and influence of the number of boosting steps. Zurich Open Repository and Archive (University of Zurich). 1–34. 3 indexed citations

18.

Belotti, Elisa, Luděk Bufka, Helmut Küchenhoff, et al.. (2015). Patterns of Lynx Predation at the Interface between Protected Areas and Multi-Use Landscapes in Central Europe. PLoS ONE. 10(9). e0138139–e0138139. 19 indexed citations

19.

Gröbe, H, et al.. (1988). [Continuous subcutaneous insulin infusion in the treatment of insulin-dependent diabetes mellitus].. PubMed. 136(2). 81–4. 1 indexed citations

20.

Seibold, Heidi & F Michot. (1972). [Influenza C induced autoimmunohemolytic anemia. Exacerbation caused by a virus or a chronic hemolytic anemia?].. PubMed. 102(5). 173–6. 1 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.

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