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Testing the Fairness of a Coin by Akaike’s Information Criterion

Testing the Fairness of a Coin by Akaike’s Information Criterion
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Marker Selection by Akaike Information Criterion and Bayesian Information Criterion

Genetic Epidemiology, 2001
We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected‐affected versus affected‐unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters.
Li, W., Nyholt, D.R.
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Exponential Smoothing and the Akaike Information Criterion [PDF]

, 2009
Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle.
Ralph D. Snyder, J. Keith Ord
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Akaike's Information Criterion in Generalized Estimating Equations

Biometrics, 2001
Summary. Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model‐selection criteria available in GEE. The well‐known Akaike Information Criterion (AIC)
Wei Pan
exaly   +3 more sources

Robust Akaike Information Criterion for ARMA models

, 2004
A robust version of the Akaike Information Criterion (AIC) [5] is defined to the aim of selecting the order of an ARMA model. It is an extended version of the classical criterion based on weighted likelihood methodology [12]. To achieve robustness a weight is associated to each component of the conditional log–likelihood [3].
AGOSTINELLI C., AGOSTINELLI, Claudio
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A note on the corrected Akaike information criterion for threshold autoregressive models

Journal of Time Series Analysis, 1998
A bias-corrected Akaike information criterion AICC is derived for self-exciting threshold autoregressive (SETAR) models. The small sample properties of the Akaike information criteria (AIC, AICC) and the Bayesian information criterion (BIC) are studied ...
Li, WK, Wong, CS
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Extending the Akaike Information Criterion to Mixture Regression Models

Journal of the American Statistical Association, 2007
We examine the problem of jointly selecting the number of components and variables in finite mixture regression models. We find that the Akaike information criterion is unsatisfactory for this purpose because it overestimates the number of components ...
Prasad A Naik, Chih-Ling Tsai
exaly   +2 more sources

Akaike's Information Criterion and the Histogram

Biometrika, 1987
By interpreting the histogram as a step-function, we explore the use of Akaike's information criterion in an automatic procedure to determine the histogram class width. We obtain an asymptotic relationship and present some results from a small simulation study.
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