Model Selection and Inference

By S. Mudivarthy, M. Bhaskara Rao. Technometrics, 2000

1,291 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c244ddcef3b56287eec014471f998a43befa6038

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In Artificial Intelligence

Citations in the corpus

Assessing the fit of site-occupancy models (2004, methodology): “The overdispersion parameter ˆ c can also be estimated so that in situations where even the most global model is found to be a poor fit of the data, the quasi-likelihood version of AIC (QAIC) may be used for model selection ( Burnham and Anderson 1998, p. 53 ) and parameter standard errors may be in”
Flight distance of urban birds, predation, and selection for urban life (2008, methodology): “I used multiple regression to find the best-fit model, using the software JMP (2000), using Akaike’s information criterion as an estimate of the improvement in fit for addition of variables to the models reported in Tables 2 and 3 (Burnham and Anderson 1998).”
Testing Similarity Effects with Dyadic Response Surface Analysis (2018, methodology): “One of the most commonly used is the Akaike information criterion (AIC; for an overview, see Burnham & Anderson, 1998).”
What are the best correlates of predicted extinction risk (2004, methodology): “The Akaike Information Criterion (AICc) (Burnham and Anderson, 1998) and Mallow s Cp statistics (Draper and Smith, 1981) were used as objective means of selecting the most parsimonious multiple regression model from a set of candidate models of varying complexity (in terms of number of fitted parame”
Optimal use of resources structures home ranges and spatial distribution of black bears (2007, methodology): “Our observations were statistically independent (ĉ ¼ 0.98), so we did not include a variance inflation factor in our analyses (Burnham & Anderson 2002).”
A survey of Bayesian predictive methods for model assessment, selection and comparison (2012, methodology): “TIC, RIC, NIC Takeuchi (1976) (since the original paper is in Japanese, see also, e.g., Shibata (1989); Burnham and Anderson (1998)) provided a more general derivation giving a better estimate for the expected prediction error if the candidate models are not particularly close to the true model…”
Constraints on control: factors influencing reproductive success in male mandrills (Mandrillus sphinx) (2005, positive): “We used the backward model selection procedure ( Burnham and Anderson, 1998 ) to select a set of explanatory variables.”