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A modified akaike information criterion
1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, 1978A method, closely related to Akaike's Information Criterion (AIC), is introduced that more nearly matches practical methods of estimating the parameters of an autoregressive (AR) model of a stationary time series. The method is computationally similar to AIC, and in preliminary experiments has shown considerable success in identifying AR model orders.
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Properties of the Akaike information criterion
Microelectronics Reliability, 1996The paper gives the origins of AIC and discusses the main properties of this measure when it is applied to continuous and discrete models. It is illustrated that AIC is not a measure of informativity because it fails to have some expected properties of information measures.
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The Akaike Information Criterion with Parameter Uncertainty
Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006., 2006An instance crucial to most problems in signal processing is the selection of the order of a candidate model. Among the different exciting criteria, the two most popular model selection criteria in the signal processing literature have been the Akaike's criterion AIC and the Bayesian information criterion BIC. These criteria are similar in form in that
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Akaike Information Criterion Statistics.
Journal of the Royal Statistical Society. Series A (Statistics in Society), 1988Daniel G. Brooks +3 more
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Relevance units machine based on Akaike's information criterion
SPIE Proceedings, 2009The relevance vector machine (RVM) is a sparse regression kernel model. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). Relevance vectors and support vectors are both selected from the input vector set. This may limit model flexibility.
Jun Zhang, Junbin Gao, Jinwen Tian
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Comparing models using Akaike’s Information Criterion (AIC)
2004Abstract The previous chapter explained how to compare nested models using an F test and choose a model using statistical hypothesis testing. This chapter presents an alternative approach that can be applied to both nested and non-nested models, and which does not rely on P values or the concept of statistical significance.
Harvey Motulsky, Arthur Christopoulos
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Application of Akaike information criterion to evaluate warfarin dosing algorithm
Thrombosis Research, 2010Several factors responsible for inter-individual differences in response to warfarin have been confirmed; however, unidentified factors appear to remain. The purpose of this study was to examine a simple method to evaluate whether optional variables are appropriate as factors to improve dosing algorithms.All patients were Japanese.
Takumi, Harada +11 more
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Conditional Akaike information criterion in the Fay–Herriot model
Statistical Methodology, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Geophysical model discrimination using the Akaike information criterion
IEEE Transactions on Automatic Control, 1981A general model building procedure is developed for employing the Akaike information criterion (AIC) to select the most appropriate stochastic model to describe a specified geophysical time series. To demonstrate the effectiveness of the proposed approach to model construction, formulas for the AIC are given for many types of stochastic models and the ...
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Bayesian derivation of Akaike's information criterion
1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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