Quantifying variable importance in a multimodel inference framework

By Xingli Giam, Julian D. Olden. 2016

111 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/68c524a658194d49d02ffc1a4144ab2ce55bf95a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. F. Dechaume‐Moncharmont
2. Mark A. F. Gillingham
3. M. Galipaud

In Ecology, Evolution, Behavior and Systematics

1. F. Dechaume‐Moncharmont
2. Mark A. F. Gillingham
3. M. Galipaud

In Nature and Landscape Conservation

1. F. Dechaume‐Moncharmont
2. Mark A. F. Gillingham
3. M. Galipaud

Citations in the corpus

• A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017, methodology): “Although Giam and Olden (2016) did not explain pre - cisely in which direction our procedure could have biased our results, they argued that it was inappropriate to study SW reliability as an es - timate of variable relative importance.”