Further analysts of the data by akaike' s information criterion and the finite corrections

By N. Sugiura. 1978

How researchers see this paper

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

1. P. Hopkins
2. C. Conroy
3. Michele Moresco
4. Simon D. M. White
5. M. Cappellari
6. R. Somerville
7. T. Naab
8. M. Ouchi

Citations in the corpus

• Forecast combinations: An over 50-year review (2022, methodology): “Criteria-based weights Information criteria, such as Akaike’s information criterion (AIC, Akaike, 1974), the corrected Akaike information criterion (AICc, Sugiura, 1978), and the Bayesian information criterion (BIC, Schwarz, 1978), are often used for model selection in forecasting.”
• Phylogenetic position and revised classification of Acacia s.l. (Fabaceae: Mimosoideae) in Africa, including new combinations in Vachellia and Senegalia (2013, unknown): “A hierarchical likelihood ratio test implemented in MODELTEST v.3.06 (Posada & Crandall, 1998) was used to determine the appropriate substitution model for each of the three plastid gene sequences based on the Akaike information criterion (AIC; Sugiura, 1978).”
• A survey of cross-validation procedures for model selection (2009, unknown): “• With the log-likelihood contrast, AIC (Akaike Information Criterion, Akaike, 1973) and its corrected versions (Sugiura, 1978; Hurvich and Tsai, 1989).”
• Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, methodology): “The best model was therefore selected under the criterion of lowest Akaike information criterion, using the version corrected (AICc) for finite samples by Sugiura (1978). This procedure was carried out separately for each response variable y = (GC, LA, BALM, NSLM).”
• Criteria for Linear Model Selection Based on Kullback's Symmetric Divergence (2004, unknown): “The corrected Akaike information criterion (AICc) is an adjusted version of AIC, originally proposed for linear regression with normal errors (Sugiura, 1978; Hurvich & Tsai, 1989).”
• TESTING FOR DIFFERENT RATES OF CONTINUOUS TRAIT EVOLUTION USING LIKELIHOOD (2006, methodology): “The distance between the true model and the approximating model is approximated by the Akaike Information Criterion (AIC) value (Akaike 1973), or the second-order information criterion (AICc, Sugiura 1978); the best model is the one with the lowest value.”
• Information criteria for astrophysical model selection (2007, unknown): “This is given by (Sugiura 1978) AICc = AIC + 2k(k + 1) N − k − 1 .”
• Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, methodology): “The method provides a quantitative expression of these distances via AIC (Akaike 1973), or via its corrected version for small sample sizes (AICc, Sugiura 1978), or for overdispersed data (QAIC, Lebreton et al. 1992).”
• Exploring the model space and ranking a best class of models in surface-wave dispersion inversion: Application at European strong-motion sites (2012, unknown): “In case of least-squares estimation with normally distributed errors and for small sample sizes, Sugiura (1978) and Hurvich and Tsai (1989) propose a corrected version of equation 2 expressed as AICc ¼ nf ⋅ lnðê2Þ þ 2K þ ð2KðK þ 1Þ∕ðnf − K − 1ÞÞ; (3) where nf is the total number of observations…”