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- W2032082883 abstract "In the present article, we consider the variable selection problem in Poisson regression models. Akaike's information criterion (AIC) is the most commonly applied criterion for selecting variables. However, the bias of the AIC cannot be ignored, especially in small samples. We herein propose a new bias-corrected version of the AIC that is constructed by stochastic expansion of the maximum likelihood estimator. The proposed information criterion can reduce the bias of the AIC from O(n−1) to O(n−2). The results of numerical investigations indicate that the proposed criterion is better than the AIC." @default.
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- W2032082883 date "2013-06-01" @default.
- W2032082883 modified "2023-10-18" @default.
- W2032082883 title "Bias-Corrected AIC for Selecting Variables in Poisson Regression Models" @default.
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- W2032082883 doi "https://doi.org/10.1080/03610926.2011.600504" @default.
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