Carolin Strobl

Statistics and Probability · 2007–2021

A researcher in the Science corpus: 8 papers; trusts 36 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 73 citations into it; #2,519 of 12,932 by trust here, #1,989 by citations
• Artificial Intelligence — 45 citations into it; #6,877 of 35,345 by trust here, #6,796 by citations
• Experimental and Cognitive Psychology — 19 citations into it
• Statistical Learning Theory — 18 citations into it; #608 of 1,038 by trust here, #770 by citations
• Molecular Biology — 17 citations into it; #2,110 of 17,773 by trust here, #4,409 by citations
• Information Systems — 16 citations into it; #1,400 of 8,771 by trust here, #1,175 by citations
• Management Science and Operations Research — 16 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Carolin Strobl trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Ross Quinlan
2. Achim Zeileis
3. Jimmy de la Torre
4. Torsten Hothorn
5. Trevor J. Hastie
6. Leo Breiman
7. Niko Speybroeck
8. Eric R. Ziegel
9. Kurt Hornik
10. Jerome H. Friedman

Who trusts Carolin Strobl overall

1. Gilles Louppe
2. Wei-Yin Loh
3. Marco Sandri
4. Jalal Al-Tamimi
5. Alexander Hapfelmeier
6. Ulrike Grömping
7. E. Buechley
8. Anne-Laure Boulesteix
9. Max Kuhn
10. Edgar C. Merkle

Papers from Carolin Strobl's perspective

Ordered by how strongly the researchers Carolin Strobl trusts endorse each paper. Same corpus, a different order for every researcher.

1. Random Forests
2. The Elements of Statistical Learning
3. C4.5: Programs for Machine Learning
4. Bagging Predictors
5. The Nature of Statistical Learning Theory
6. Regression Shrinkage and Selection via the Lasso
7. Analyzing Bagging
8. On bagging and nonlinear estimation
9. The EM algorithm and extensions
10. Generalized Linear Models

Their papers

• Unbiased split selection for classification trees based on the Gini Index (2007) — 319 citations
• Bias in random forest variable importance measures: Illustrations, sources and a solution (2007) — 1,433 citations
• The behaviour of random forest permutation-based variable importance measures under predictor correlation (2010) — 351 citations
• An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests (2009) — 2,461 citations
• A new variable importance measure for random forests with missing data (2012) — 201 citations
• Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis (2021) — 114 citations
• On the Estimation of Standard Errors in Cognitive Diagnosis Models (2018) — 58 citations
• Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning (2011) — 96 citations