Akaike Information Criterion Statistics

By D. Grove, Y. Sakamoto, Makio Ishiguro, G. Kitagawa. 1988

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Citations in the corpus

Generalized linear and generalized additive models in studies of species distributions: setting the scene (2002, methodology): “Variable selection is basically the same for all the described regression models, although evaluation criteria like the Akaike Information Criterion (AIC, Akaike, 1973; see also Sakamoto et al., 1988 ) can be used with GLMs and GAMs.”
Spinner dolphins in a remote Hawaiian atoll: social grouping and population structure (2005, methodology): “Models of the temporal permanence of associations were fitted to the data using maximum likelihood and Akaike information criterion (AIC) methods to determine best fit (Sakamoto et al., 1986).”
Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection (2001, methodology): “Although there is a rich body of work relating to Akaike’s methods (Akaike, 1974; Stone, 1977; Taylor, 1987; Sakamoto et al., 1986; Sakamoto, 1992; Hurvich et al., 1998; Bozdogan, 2000; Cavanaugh & Shumway, 1998), especially in time series analysis (Akaike, 1979; Atkinson, 1978; Shibata, 1976), we”
Biased adult sex ratio can affect fecundity in primiparous moose Alces alces (2002, methodology): “We considered the model with the lowest Akaike information criterion (AIC) score to be most parsimonious ( Sakamoto, Ishiguro & Kitagawa 1986 ).”
Habitat heterogeneity affects population growth in goshawk Accipiter gentilis (2001, methodology): “Following convention, we considered values of (AICc larger than unity statistically significant ( Sakamoto, Ishiguro & Kitagawa 1986 ).”