Models for Overdispersed Data in Entomology

By Clarice G. B. Dem'etrio, John P. Hinde, Rafael de Andrade Moral. 2014

111 citations recorded by the source corpus.

Entomological data are often overdispersed, characterised by a larger variance than assumed by simple standard models. It is important to model overdispersion properly in order to avoid incorrect and misleading inferences. Outcomes of interest are often in the form of counts or proportions and we present extended models that incorporate overdispersion, methods to assess its impact and model goodness-of-fit, and techniques to test treatment differences in the presence of overdispersion.

Subfields: Insect Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/153449d45cb507dff10460505b36aaab2464e6b3

How researchers see this paper

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

In Insect Science

1. Idemauro Antonio Rodrigues de Lara
2. Rafael de Andrade Moral
3. Susanna Tall
4. Í. Delalibera
5. N. V. Meyling
6. Fernanda Canassa

In Plant Science

1. Rafael de Andrade Moral
2. John P. Hinde
3. Í. Delalibera
4. N. V. Meyling
5. Clarice G. B. Dem'etrio

Citations in the corpus

• Half-Normal Plots and Overdispersed Models in R: The hnp Package (2017, background): “Further details on overdispersion models for count and proportion data may be found in Hinde and Demétrio (1998) and Demétrio et al. (2014).”
• Effects of bean seed treatment by the entomopathogenic fungi Metarhizium robertsii and Beauveria bassiana on plant growth, spider mite populations and behavior of predatory mites (2019, background): “Because the string bean data presented overdispersion (Demétrio et al., 2014), i.”