Model selection and multi-model inference

By K. Burnham, David R. Anderson. 1998

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Citations in the corpus

Subspace Regularizers for Few-Shot Class Incremental Learning (2021, background): “In the absence of any positive examples of those classes, performance on base classes will suffer due to catastrophic forgetting (Goodfellow et al., 2013), while performance on novel classes will likely be poor as a result of overﬁtting (Anderson & Burnham, 2004).”
Variable Selection for Clustering with Gaussian Mixture Models (2009, unknown): “The selected model is (K̂ = 6, m̂ = [pLI], Ŝ = (3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19), Ĵ = (8, 11, 15)).”
Model Averaging and Its Use in Economics (2017, methodology): “In particular, the R packages MuMIn (Bartoń, 2016) and AICcmodavg (Mazerolle, 2017) can handle a wide range of different models.”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, methodology): “5, which considers bias and variance of the averaged models (for derivation see Burnham and Anderson 2002:159–162) varð eY Þ ¼ XM m¼1 wm ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi varð bYmÞ þ c2mq !”
Supervised Representation Learning: Transfer Learning with Deep Autoencoders (2015, methodology): “Given two probability distributions P ∈ R k×1 and Q ∈ R k×1 , the KL divergence of Q from P is the information lost when Q is used to approximate P [Liddle et al., 2010], defined as D KL (P |”
Site occupancy models in the analysis of environmental DNA presence/absence surveys: a case study of an emerging amphibian pathogen (2013, methodology): “In PRESENCE, we fitted the classic two-level site occupancy model (MacKenzie et al. 2002; Tyre et al. 2003) to the data and ranked candidate models according to Akaike Information Criterion (AIC; Burnham & Anderson 2002).”
Quantifying the effects of prey abundance on killer whale reproduction (2009, methodology): “We examined the performance of Bayesian (Bayes factor, Kass & Raftery 1995) and likelihood (AIC, Burnham and Anderson 2002) model selection tools, as each has limitations (Link & Barker 2006; Ward 2008).”