James D. Malley

Statistics and Probability · 2009–2016

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 25 citations into it
• Artificial Intelligence — 20 citations into it; #15,590 of 35,345 by trust here, #10,566 by citations
• Genetics — 19 citations into it; #3,420 of 6,854 by trust here, #3,711 by citations
• Molecular Biology — 19 citations into it; #4,146 of 17,773 by trust here, #5,655 by citations
• Statistical Learning Theory — 10 citations into it
• Information Systems — 8 citations into it; #3,005 of 8,771 by trust here, #1,515 by citations
• Evaluation & Benchmarks — 5 citations into it

Whom James D. Malley trusts overall

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

1. Eric R. Ziegel
2. J. Ross Quinlan
3. Niko Speybroeck
4. Heather J. Cordell
5. Torsten Hothorn
6. Trevor J. Hastie
7. R. Azen
8. Ryan J. Urbanowicz
9. Leo Breiman
10. Andy Liaw

Who trusts James D. Malley overall

1. Jalal Al-Tamimi
2. E. Buechley
3. Edgar C. Merkle
4. Marvin N. Wright
5. X. Chen
6. Yoed N. Kenett
7. Harald Baayen
8. Ben Ambridge
9. Florian Brühlmann
10. Johan A. Oldekop

Papers from James D. Malley's perspective

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

1. The Elements of Statistical Learning
2. Identifying SNPs predictive of phenotype using random forests (Genetic Epidemiology)
3. Consistency of Random Forests and Other Averaging Classifiers
4. Detecting gene-gene interactions that underlie human diseases (Nature reviews genetics)
5. Bias in random forest variable importance measures: Illustrations, sources and a solution
6. Classification and regression trees
7. Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions
8. Classification and Regression by randomForest
9. Random Forests
10. GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures

Their papers

• 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
• Detecting gene-gene interactions using a permutation-based random forest method (2016) — 65 citations