王艳君,刘群,任一平

• 1:中国海洋大学水产学院
• 2:中国海洋大学水产学院
• 3:中国海洋大学水产学院 山东青岛266003
• 4:山东青岛266003

摘要(Abstract)：

由于渔业资源评估中补充量的剧烈变动、亲体量的测量误差以及时间序列的偏差常常使亲体 补充量(SR)关系模型的确定存在很大偏差问题。本文以7种SR(Stock Recruitment)模型的模拟数据作为观测数据,研究了AIC(AkaikeInfor mationCriterion)与BIC(BayesianInformationCriterion)在SR模型选择中的应用。作为例证,文中采用AIC和BIC对8组实际的SR数据进行了SR模型的选择,并对其结果进行了比较。参数的估计方法为最大似然法(Maximumlikelihoodmethod)。结果表明,AIC和BIC在SR模型选择中是有效的。但是,对于嵌套模型,BIC可能比AIC更有效。

关键词(KeyWords)： 亲体-补充量模型;最大似然法;AIC(Akaike Information Criterion);BIC(Bayesian Information Criterion)

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目 (30 2 71 0 2 5)资助

作者(Author): 王艳君,刘群,任一平

DOI: 10.16441/j.cnki.hdxb.2005.03.009

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