Department of Electrical Engineering
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Browsing Department of Electrical Engineering by Subject "Akaike information criterion"
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Item Regularized Information Loss for Improved Model Selection(Springer Science and Business Media Deutschland GmbH, 2023) Kamalov, Firuz; Moussa, Sherif; Reyes, Jorge AvanteInformation criteria are used in many applications including statistical model selection and intelligent systems. The traditional information criteria such as the Akaike information criterion (AIC) do not always provide an adequate penalty on the number of model covariates. To address this issue, we propose a novel method for evaluating statistical models based on information criterion. The proposed method, called regularized information criterion (RIL), modifies the penalty term in AIC to reduce model overfitting. The results of numerical experiments show that RIL provides a better reflection of model predictive error than AIC. Thus, RIL can be a useful tool in model selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.