A unified approach to model selection using the likelihood ratio test

By Fraser Iain Lewis, Adam P. Butler, L. Gilbert. 2011

274 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/4ab654554c37318bebb18bb6e57d5550e0f2b61e

How researchers see this paper

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

In Artificial Intelligence

1. Qing Mai
2. Sotirios V. Archontoulis
3. Fernando E. Miguez
4. Hui Zou
5. Jianqing Fan
6. Jinchi Lv
7. Cun-Hui Zhang
8. Yingying Fan

In Ecological Modeling

1. Qing Mai
2. Sotirios V. Archontoulis
3. Fernando E. Miguez
4. Hui Zou

In Nature and Landscape Conservation

1. Qing Mai
2. Sotirios V. Archontoulis
3. Fernando E. Miguez
4. Hui Zou

Citations in the corpus

• The fused Kolmogorov filter: A nonparametric model-free screening method (2014, methodology): “It requires sophisticated estimation techniques, strong model assumpti ons and often advanced computing algorithms (Tibshirani 1996, Fan & Li 2001, Lv & Fa n 2009, Zhang 2010). Because variable screening deals with a much less amb itious goal, it is possible that sure screening could be achieved by ”
• Spatial scales of marine conservation management for breeding seabirds (2018, background): “already accounted for by other fixed or random effects [70].”
• Nonlinear Regression Models and Applications in Agricultural Research (2015, methodology): “Depending on the structure of the models, different statistical criteria can be used to find the best model: F test, Akaike information criterion (AIC), Bayesian information criterion (BIC), or the likelihood ratio test (Zucchini, 2000; Burnham and Anderson, 2002; Hoffmann, 2005; Ritz and Streibig, ”