Akaike information criteria; AIC
AICc key references
Kenneth P. Burnham, David R. Anderson. 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research 33: 261. http://faculty.washington.edu/skalski/classes/QERM597/papers_xtra/Burnham%20and%20Anderson.pdf 1767 citations at Google Scholar UCSD QH323.5
Kenneth P Burnham, David R Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach. Springer. Cited by 20675 pdf Books, Geisel Floor1 East QH323.5 .B87 1998 AVAILABLE SIO, Geisel Floor1 East QH323.5 .B87 2002 DUE 12-06-14
Kenneth P Burnham. 2002. Model Selection And Inference. New York : Springer. SIO, Geisel Floor1 East QH323.5 .B87 2002 DUE 12-06-14
Halbert White. 1994. Estimation, Inference and Specification Analysis. Econometric Society Monographs.
David R. Anderson 2008. Model-Based Inferences in the Social Sciences: A primer on Evidence. Springer.
Peter J. Waddell, Xi Tan. 2013 (Submitted on 31 Dec 2012). New g%AIC, g%AICc, g%BIC, and Power Divergence Fit Statistics Expose Mating between Modern Humans, Neanderthals and other Archaics.
- Abstract: The purpose of this article is to look at how information criteria, such as AIC and BIC, relate to the g%SD fit criterion derived in Waddell et al. (2007, 2010a). The g%SD criterion measures the fit of data to model based on a normalized weighted root mean square percentage deviation between the observed data and model estimates of the data, with g%SD = 0 being a perfectly fitting model. However, this criterion may not be adjusting for the number of parameters in the model comprehensively. Thus, its relationship to more traditional measures for maximizing useful information in a model, including AIC and BIC, are examined. This results in an extended set of fit criteria including g%AIC and g%BIC. Further, a broader range of asymptotically most powerful fit criteria of the power divergence family, which includes maximum likelihood (or minimum G^2) and minimum X^2 modeling as special cases, are used to replace the sum of squares fit criterion within the g%SD criterion. Results are illustrated with a set of genetic distances looking particularly at a range of Jewish populations, plus a genomic data set that looks at how Neanderthals and Denisovans are related to each other and modern humans. Evidence that Homo erectus may have left a significant fraction of its genome within the Denisovan is shown to persist with the new modeling criteria.
Freedman, D. A. (1983) "A note on screening regression equations." The American Statistician, 37, 152–155. Freedman's Paradox
- http://stat.ethz.ch/R-manual/R-patched/library/stats/html/AIC.html Generic function calculating Akaike's ‘An Information Criterion’ for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + k*npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC, or k = log(n) (n being the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion).
Pages in category "AICc"
The following 15 pages are in this category, out of 15 total.