Information Criteria and Statistical Modeling

By Sadanori Konishi, G. Kitagawa. 2007

931 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c9976a0360654b4508a8dc78d039ab73b32d57f

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In Artificial Intelligence

Citations in the corpus

Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, background): “However, the approximation step is based on the assumption that the true data-generation process is contained in the candidate model (see Chapter 4 of Konishi and Kitagawa, 2007, for details).”
Dynamical decoupling for superconducting qubits: A performance survey (2022, background): “[103, pp. 51-74].”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “There are several books available on model selection methods in general and on AIC–IT-based approaches in particular that can serve as a primary source for the detailed statistical background (Linhart and Zucchini 1986; Sakamoto 1991; Royall 1997; McQuarrie and Tsai 1998; Burnham and Anderson 2002; ”
A Bayesian information criterion for singular models (2013, methodology): “Information criteria are valuable tools for model selection (Burnham and Anderson, 2002; Claeskens and Hjort, 2008; Konishi and Kitagawa, 2008).”
The Copula Information Criteria (2014, background): “The GIC development concerns reaching an expansion for n that takes into consideration the general form of T .”
AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons (2011, background): “but from a coding theory standpoint, whereas Konishi and Kitagawa (2007) continue to emphasize the link between information theory and statistical modeling.”
Predictive Analytics in Information Systems Research (2011, neutral): “For example, Konishi and Kitagawa (2007, p. 2) note, There may be no significant difference between the point of view of inferring the true structure and that of making a prediction if an infinitely large quantity of data is available [and] if the data are noiseless.”
Estimation of prediction error by using K-fold cross-validation (2011, methodology): “As seen in the calculation of information criteria (Konishi and Kitagawa 2007), TRN − sN is decomposed into the following three terms: TRN − sN = N−1Ψ (D; θ̂ ) − ∫ Ψ (z; θ̂ )dF(z) = { N−1Ψ (D; θ̂ ) − N−1Ψ (D; θ0) } + { N−1Ψ (D; θ0) − ∫ Ψ (z; θ0)dF(z) } + {∫ Ψ (z; θ0)dF(z) − ∫ Ψ (z; θ̂ )dF(z) } .”
Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification (2010, methodology): “…Akaike Information Criterion (AIC) and variations/extensions thereof, such as the Bayesian (BIC), the Schwarz (SIC), the Hannan-Qinn (HQIC) and the Minimum Description Length (MDL), as well as Cross-Validation criteria; Rao and Wu (2001), Burnham and Anderson (2002), Konishi and Kitagawa (2008).”
Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models (2016, methodology): “Under this setup, the cBIC is essentially the first-order Laplace approximation of−2log{ f (y)} (Konishi and Kitagawa 2007, Chapter 9).”
Multimodel inference in ecology and evolution: challenges and solutions (2011, methodology): “…& Hjort, 2003; Claeskens et al., 2007), Akaike’s Bayesian Information Criterion, the Generalized Information Criterion (GIC), the Extended (Bootstrap) Information Criterion (EIC), the Predictive Information Criterion and Takeuchi’s Information criterion [TIC; reviewed in Konishi & Kitagawa (2008)].”