Fitting complex ecological point process models with integrated nested Laplace approximation

By J. Illian, Sara Martino, Sigrunn Holbek Sørbye, J. B. Gallego-Fernández, M. Zunzunegui, M. Esquivias, Justin M. J. Travis. 2013

103 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/27b219db9eb380584ad94429b4dc67cd4bb1f5db

How researchers see this paper

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

In Applied Mathematics

1. G. Guillera‐Arroita
2. Mevin B. Hooten
3. T. Hefley
4. J. Fieberg

In Environmental Engineering

1. G. Guillera‐Arroita
2. J. Fieberg
3. Mevin B. Hooten
4. Stefanie Muff
5. T. Hefley
6. Anthony R. Ives

In Geometry and Topology

1. G. Guillera‐Arroita
2. Mevin B. Hooten
3. T. Hefley
4. J. Fieberg

Citations in the corpus

• Data Integration for Large-Scale Models of Species Distributions. (2020, background): “processes [59], although this efficiency comes at a cost of less flexibility in what models can be fitted”
• Hierarchical Species Distribution Models (2016, methodology): “If sampling error is ignored, the Poisson point process model is relatively straightforward to implement using maximum likelihood, penalized maximum likelihood, or Bayesian estimation (via MCMC or integrated nested Laplace approximation [57, 58]).”
• Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation (2018, background): “, 2016), and for modelling species distributions more generally (Illian et al., 2013; Bakka et al., 2016).”