モデル選択手法の水産資源解析への応用 : 情報量規準とステップワイズ検定の取り扱い Application of Methods for Model Selection to Fish Stock Analyses : Dealing with Information Criteria and Stepwise Tests

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著者

    • 庄野 宏 SHONO Hiroshi
    • 独立行政法人水産総合研究センター 遠洋水産研究所 National Research Institute of Far Seas Fisheries, Fishery Research Agency

抄録

Akaike's information criterion, which is widely used as a criterion of model selection in fish population dynamics, tend to overestimate the number of unknown parameters in several cases. In this paper, we discuss the selection performance to select the true model of various information criteria (AIC, BIC, c-AIC, TIC and HQ etc.) through computer simulations by analysis of variance, linear regression and Gaussian mixture model corresponding to CPUE standardization. As a result, we obtain the following results with regard to the goodness of various information criteria.<BR>1) In small samples or in the case that there are many unknown parameters compared to the sample size, the selection performance of c-AIC (finite correction of AIC) is superior to that of other information criteria.<BR>2) In large samples, the consistent information criteria, BIC and HQ, are better than AIC.<BR>3) In nested ANOVA-type model, the selection performance of TIC, which is exact evaluation of AIC, is almost same as that of AIC and c-AIC, and BIC is slightly good compared to stepwise F-test.<BR>4) In normal mixture model, stepwise chi-square test is not theoretically applicable and the selection performance of Bayes-type information criterion assuming the Dilichlet prior is superior to that of AIC and BIC.

収録刊行物

  • 計量生物学  

    計量生物学 27(1), 55-67, 2006-06-30 

    The Biometric Society of Japan

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各種コード

  • NII論文ID(NAID)
    10018012975
  • NII書誌ID(NCID)
    AA11591618
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    0918-4430
  • NDL 記事登録ID
    8007740
  • NDL 雑誌分類
    ZR1(科学技術--生物学)
  • NDL 請求記号
    Z74-B725
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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