Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

By Adam Duarte, Michael J. Adams, J. Peterson. 2018

109 citations recorded by the source corpus.

Subfields: Ecology, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c43d9b53c0d10a78f8b6ea2c99dd4b187128c49c

How researchers see this paper

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

In Ecology

1. Jonas Knape
2. Debora Arlt
3. Mathieu Chevalier
4. F. Barraquand
5. Åke Berg
6. Alejandro Ruete
7. M. Żmihorski
8. T. Pärt

In Genetics

1. Åke Berg

In Nature and Landscape Conservation

1. Jonas Knape
2. F. Barraquand
3. Åke Berg

Citations in the corpus

• Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit (2018, result): “…(Knape & KornerNievergelt, 2015; Barker et al., 2018) and our results, and some results of previous studies (Martin et al., 2011; Toribio, Gray, & Liang, 2012; Duarte et al., 2018), show that this leads to N-mixture models often being sensitive to even small amounts of model mis-specification.”