Jin Li
Environmental Engineering · Environmental Modelling & Software · 2011–2014
A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 31 citations into it; #1,610 of 5,479 by trust here, #2,691 by citations
- Ecology — 10 citations into it
- Economics and Econometrics — 10 citations into it
- Artificial Intelligence — 9 citations into it
- Civil and Structural Engineering — 8 citations into it
- Global and Planetary Change — 7 citations into it
- Management, Monitoring, Policy and Law — 6 citations into it
- Nature and Landscape Conservation — 6 citations into it
Whom Jin Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tomislav Hengl
- Edzer J. Pebesma
- Andrew D. Heap
- Rana Moyeed
- R. M. Lark
- Jorge Kazuo Yamamoto
- Aaron M. Ellison
- Roger S. Bivand
- Tilmann Gneiting
- William N. Venables
Who trusts Jin Li overall
- Andrew D. Heap
- U. Bronowicka-Mielniczuk
- Tomislav Hengl
- Somayeh Dodge
- J. Mielniczuk
- Madlene Nussbaum
- Aleksandar Sekulić
- Gerard B. M. Heuvelink
- Gil Bohrer
- H. Keskin
Papers from Jin Li's perspective
Ordered by how strongly the researchers Jin Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Bagging Predictors
- An Empirical Comparison of Kriging Methods for Nonlinear Spatial Point Prediction
- Generalized Linear Models
- Modern Applied Statistics with S-Plus.
- Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction (Ecosystems)
- Mixed-Effects Models in S and S-Plus
- Multivariable geostatistics in S: the gstat package (Computational Geosciences)
- On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML
- Random Forests
- Spline Models for Observational Data
Their papers
- Spatial interpolation methods applied in the environmental sciences: A review (2014) — 868 citations
- Application of machine learning methods to spatial interpolation of environmental variables (2011) — 348 citations
- A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors (2011) — 962 citations