• Title/Summary/Keyword: QAIC

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Using the corrected Akaike's information criterion for model selection (모형 선택에서의 수정된 AIC 사용에 대하여)

  • Song, Eunjung;Won, Sungho;Lee, Woojoo
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.119-133
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    • 2017
  • Corrected Akaike's information criterion (AICc) is known to have better finite sample properties. However, Akaike's information criterion (AIC) is still widely used to select an optimal prediction model among several candidate models due to of a lack of research on benefits obtained using AICc. In this paper, we compare the performance of AIC and AICc through numerical simulations and confirm the advantage of using AICc. In addition, we also consider the performance of quasi Akaike's information criterion (QAIC) and the corrected quasi Akaike's information criterion (QAICc) for binomial and Poisson data under overdispersion phenomenon.