Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution
Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In context...
| Published in: | Frontiers in Ecology and Evolution |
|---|---|
| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10852/76660 http://urn.nb.no/URN:NBN:no-79766 https://doi.org/10.3389/fevo.2019.00415 |
| Summary: | Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale. |
|---|